-------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_2.log
  log type:  text
 opened on:  12 May 2023, 16:29:30

. 
.         
. ******************************************************************** 
. ********          TABLE 2:  POOLED IVQR                     ********
. ******************************************************************** 
.         
. 
. 
. 
.         use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
.         
. * First Panel * 
.                 
.                 local Inst "l_rail1898 l_hwy1947 l_pix1835"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln = l_rail1898 l_hwy1947 l_pix1
> 835), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    3101.93
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8742
                                                  Root MSE        =      .4856

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.315844   .0258333    50.94   0.000     1.265212    1.366477
    _Iyear_2 |   .3989198   .0469814     8.49   0.000     .3068379    .4910018
    _Iyear_3 |   .6650321   .0463963    14.33   0.000      .574097    .7559672
       _cons |     6.2805   .1725364    36.40   0.000     5.942335    6.618666
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

. 
.                 xi: ivqregress iqr l_vmt i.year (l_ln = l_rail1898 l_hwy1947 l_pix1
> 835) ,  quantile(10 25 50 75 90) bound(0 5, at(0.25)) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     684
Estimator: Inverse quantile regression                 Wald chi2(15) = 6607.16
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.490932   .0305035    48.88   0.000     1.431146    1.550718
    _Iyear_2 |   .3618336   .1076412     3.36   0.001     .1508606    .5728065
    _Iyear_3 |   .6731212   .0921355     7.31   0.000     .4925389    .8537035
       _cons |   4.549984   .2433919    18.69   0.000     4.072944    5.027023
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.399524   .0240157    58.28   0.000     1.352454    1.446594
    _Iyear_2 |   .4558514   .0502359     9.07   0.000     .3573909    .5543119
    _Iyear_3 |   .7068415   .0513768    13.76   0.000     .6061449    .8075382
       _cons |   5.486208   .1714885    31.99   0.000     5.150097     5.82232
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.278983    .020175    63.39   0.000     1.239441    1.318525
    _Iyear_2 |   .4476969   .0440042    10.17   0.000     .3614503    .5339435
    _Iyear_3 |   .6684798   .0464596    14.39   0.000     .5774206    .7595391
       _cons |   6.575133   .1443711    45.54   0.000     6.292171    6.858095
-------------+----------------------------------------------------------------
q75          |
        l_ln |    1.22884   .0207913    59.10   0.000      1.18809     1.26959
    _Iyear_2 |   .3659582   .0453389     8.07   0.000     .2770957    .4548207
    _Iyear_3 |   .6378913    .044627    14.29   0.000      .550424    .7253585
       _cons |   7.155976   .1548042    46.23   0.000     6.852565    7.459386
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.148178    .025995    44.17   0.000     1.097229    1.199127
    _Iyear_2 |   .3356849   .0557538     6.02   0.000     .2264094    .4449604
    _Iyear_3 |   .5547213   .0524589    10.57   0.000     .4519037    .6575389
       _cons |   7.959984   .1999637    39.81   0.000     7.568062    8.351905
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

. 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           63.835                 2.319
Constant effect |            8.458                 2.415
Dominance       |            0.000                 2.255
Exogeneity      |            2.774                 2.109
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           63.835                 2.097
Constant effect |            8.458                 2.086
Dominance       |            0.000                 2.001
Exogeneity      |            2.774                 1.888
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat coefplot, name (ivqr_pooled_panel_1_model_1, replace) subtitl
> e (Model 1)

.                 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln = l_rail1898 l_hwy1947
>  l_pix1835), robust                  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7467.71
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9339
                                                  Root MSE        =     .35203

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9235153   .0628895    14.68   0.000     .8002542    1.046776
       l_pop |   .4049238   .0462049     8.76   0.000     .3143639    .4954836
    _Iyear_2 |    .394456   .0338432    11.66   0.000     .3281246    .4607874
    _Iyear_3 |   .6290067   .0337278    18.65   0.000     .5629015    .6951119
       _cons |   3.676959   .2288344    16.07   0.000     3.228452    4.125467
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

. 
.                 xi: ivqregress iqr l_vmt l_pop i.year (l_ln = l_rail1898 l_hwy1947 
> l_pix1835) ,  quantile(10 25 50 75 90) vce(robust)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(20) = 11667.88
                                                      Prob > chi2   =   0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.034458   .0848356    12.19   0.000     .8681834    1.200733
       l_pop |   .3844208   .0589488     6.52   0.000     .2688834    .4999583
    _Iyear_2 |   .3350816   .0933199     3.59   0.000      .152178    .5179852
    _Iyear_3 |   .5742881   .0782976     7.33   0.000     .4208277    .7277485
       _cons |   2.704193   .2664051    10.15   0.000     2.182049    3.226337
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.013244   .0661569    15.32   0.000     .8835787    1.142909
       l_pop |    .347586   .0487153     7.14   0.000     .2521057    .4430663
    _Iyear_2 |   .4743995   .0455994    10.40   0.000     .3850264    .5637726
    _Iyear_3 |   .6714199    .048549    13.83   0.000     .5762656    .7665742
       _cons |   3.567175   .2500772    14.26   0.000     3.077032    4.057317
-------------+----------------------------------------------------------------
q50          |
        l_ln |   .8566382   .0971068     8.82   0.000     .6663124    1.046964
       l_pop |    .434406   .0775132     5.60   0.000     .2824829    .5863291
    _Iyear_2 |    .393447   .0406678     9.67   0.000     .3137395    .4731544
    _Iyear_3 |   .6416122   .0424139    15.13   0.000     .5584824    .7247419
       _cons |   3.791663   .3916105     9.68   0.000      3.02412    4.559205
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .7707063   .0781608     9.86   0.000      .617514    .9238987
       l_pop |   .4663873   .0665137     7.01   0.000     .3360228    .5967517
    _Iyear_2 |    .366432   .0382029     9.59   0.000     .2915556    .4413084
    _Iyear_3 |   .5998574   .0390269    15.37   0.000      .523366    .6763488
       _cons |   4.123514   .3725313    11.07   0.000     3.393366    4.853661
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .6779882   .0740633     9.15   0.000     .5328268    .8231495
       l_pop |    .506578     .05806     8.73   0.000     .3927824    .6203736
    _Iyear_2 |   .3152832   .0411863     7.66   0.000     .2345596    .3960068
    _Iyear_3 |   .5174528   .0404686    12.79   0.000     .4381358    .5967698
       _cons |   4.448013   .3122007    14.25   0.000     3.836111    5.059915
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           12.814                 2.489
Constant effect |            3.235                 2.093
Dominance       |            0.000                 2.400
Exogeneity      |            2.499                 2.357
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           12.814                 2.335
Constant effect |            3.235                 1.881
Dominance       |            0.000                 1.994
Exogeneity      |            2.499                 2.251
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat coefplot, name (ivqr_pooled_panel_1_model_2, replace) subtitl
> e (Model 2)

. 
. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln = l_
> rail1898 l_hwy1947 l_pix1835), robust                             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   12453.28
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9450
                                                  Root MSE        =     .32124

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.031852   .0719744    14.34   0.000     .8907843    1.172919
           l_pop |   .2998401   .0573088     5.23   0.000     .1875169    .4121633
            div2 |  -.1751959   .0616247    -2.84   0.004     -.295978   -.0544137
            div3 |   .0426976   .0733614     0.58   0.561     -.101088    .1864833
            div4 |  -.0481125   .0884504    -0.54   0.586    -.2214722    .1252471
            div5 |  -.0713817   .0877872    -0.81   0.416    -.2434414    .1006779
            div6 |  -.1763737    .092465    -1.91   0.056    -.3576018    .0048543
            div7 |  -.1368482   .0992912    -1.38   0.168    -.3314554    .0577591
            div8 |   -.385249   .1234023    -3.12   0.002    -.6271131   -.1433849
            div9 |  -.3329298   .1175007    -2.83   0.005     -.563227   -.1026326
elevat_range_msa |   -.025972    .034827    -0.75   0.456    -.0942317    .0422878
  ruggedness_msa |   6.686251   2.055938     3.25   0.001     2.656687    10.71582
      heating_dd |  -.0148676   .0026431    -5.63   0.000     -.020048   -.0096872
      cooling_dd |  -.0224467     .00543    -4.13   0.000    -.0330893   -.0118042
          sprawl |   .0013096   .0018339     0.71   0.475    -.0022847    .0049039
        _Iyear_2 |   .3942304   .0307774    12.81   0.000     .3339079     .454553
        _Iyear_3 |   .6366666   .0308063    20.67   0.000     .5762873    .6970458
           _cons |   5.330718   .4859032    10.97   0.000     4.378365    6.283071
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year (l_ln = l_r
> ail1898 l_hwy1947 l_pix1835) ,  quantile(10 25 50 75 90) vce(robust)        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(85) = 27596.54
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.455946   .1661902     8.76   0.000      1.13022    1.781673
           l_pop |   .0174641   .1316416     0.13   0.894    -.2405486    .2754769
            div2 |   .2114704   .4261414     0.50   0.620    -.6237513    1.046692
            div3 |    .279679   .4265475     0.66   0.512    -.5563387    1.115697
            div4 |   .1786468   .4163317     0.43   0.668    -.6373483     .994642
            div5 |   .1468854   .4607865     0.32   0.750    -.7562395     1.05001
            div6 |   .0885506   .4567645     0.19   0.846    -.8066913    .9837924
            div7 |   .1252065   .4699072     0.27   0.790    -.7957947    1.046208
            div8 |  -.5766645   .4583954    -1.26   0.208    -1.475103     .321774
            div9 |  -.0210612   .5564706    -0.04   0.970    -1.111724    1.069601
elevat_range_msa |   -.127842   .0614771    -2.08   0.038    -.2483348   -.0073492
  ruggedness_msa |   15.17115   3.894818     3.90   0.000     7.537445    22.80485
      heating_dd |  -.0210542    .006347    -3.32   0.001    -.0334941   -.0086144
      cooling_dd |  -.0302228   .0120424    -2.51   0.012    -.0538255   -.0066202
          sprawl |   .0038828   .0038307     1.01   0.311    -.0036251    .0113908
        _Iyear_2 |    .409153   .0461374     8.87   0.000     .3187253    .4995807
        _Iyear_3 |   .6332946   .0486157    13.03   0.000     .5380096    .7285796
           _cons |   5.712955   1.235956     4.62   0.000     3.290526    8.135385
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.192298   .1176252    10.14   0.000     .9617565    1.422839
           l_pop |   .1500219   .0925368     1.62   0.105    -.0313469    .3313908
            div2 |  -.1702907   .1441561    -1.18   0.237    -.4528314      .11225
            div3 |   .0075177   .1497303     0.05   0.960    -.2859483    .3009837
            div4 |  -.1160484   .1602983    -0.72   0.469    -.4302272    .1981305
            div5 |  -.2484576   .1715318    -1.45   0.147    -.5846536    .0877385
            div6 |  -.3154933   .1817969    -1.74   0.083    -.6718087    .0408221
            div7 |  -.2542266   .2006313    -1.27   0.205    -.6474566    .1390035
            div8 |  -.8364668   .2251066    -3.72   0.000    -1.277668   -.3952659
            div9 |  -.5959654   .2163779    -2.75   0.006    -1.020058   -.1718725
elevat_range_msa |   -.018935   .0433862    -0.44   0.663    -.1039704    .0661003
  ruggedness_msa |   10.50734   3.305059     3.18   0.001      4.02954    16.98513
      heating_dd |  -.0231679   .0042227    -5.49   0.000    -.0314443   -.0148916
      cooling_dd |  -.0365748   .0091026    -4.02   0.000    -.0544157    -.018734
          sprawl |   -.000028   .0029816    -0.01   0.993    -.0058719    .0058158
        _Iyear_2 |   .4034055   .0467607     8.63   0.000     .3117561    .4950548
        _Iyear_3 |   .6384078   .0468694    13.62   0.000     .5465456    .7302701
           _cons |   6.666274   .7671678     8.69   0.000     5.162653    8.169895
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9268413   .1065358     8.70   0.000      .718035    1.135648
           l_pop |   .3191923    .090977     3.51   0.000     .1408806    .4975039
            div2 |   -.144523    .131294    -1.10   0.271    -.4018546    .1128085
            div3 |   .1166986   .1553046     0.75   0.452    -.1876928      .42109
            div4 |  -.0425875   .1785051    -0.24   0.811     -.392451    .3072761
            div5 |  -.0578434   .1839668    -0.31   0.753    -.4184117    .3027249
            div6 |  -.0905718   .2019864    -0.45   0.654    -.4864578    .3053142
            div7 |  -.0883775    .191869    -0.46   0.645    -.4644339    .2876789
            div8 |  -.3421559   .2558685    -1.34   0.181     -.843649    .1593371
            div9 |   -.355457   .2559449    -1.39   0.165    -.8570998    .1461859
elevat_range_msa |  -.0016993   .0468855    -0.04   0.971    -.0935932    .0901946
  ruggedness_msa |   5.458233   2.939747     1.86   0.063    -.3035646    11.22003
      heating_dd |  -.0187045   .0049282    -3.80   0.000    -.0283637   -.0090454
      cooling_dd |  -.0315284   .0102916    -3.06   0.002    -.0516995   -.0113573
          sprawl |  -.0013327   .0027949    -0.48   0.633    -.0068106    .0041451
        _Iyear_2 |   .4029161   .0366879    10.98   0.000     .3310092     .474823
        _Iyear_3 |   .6647838   .0376619    17.65   0.000     .5909679    .7385998
           _cons |   6.113348   1.033642     5.91   0.000     4.087447    8.139249
-----------------+----------------------------------------------------------------
q75              |
            l_ln |    .829136    .085395     9.71   0.000     .6617648    .9965072
           l_pop |   .4341027    .071327     6.09   0.000     .2943044    .5739011
            div2 |  -.3162342   .0789037    -4.01   0.000    -.4708825   -.1615858
            div3 |  -.0040282   .0785647    -0.05   0.959    -.1580122    .1499559
            div4 |  -.1590584   .1087793    -1.46   0.144     -.372262    .0541452
            div5 |  -.0976727   .1230383    -0.79   0.427    -.3388234    .1434781
            div6 |  -.1321782    .105007    -1.26   0.208    -.3379882    .0736318
            div7 |  -.0955022   .1328814    -0.72   0.472    -.3559448    .1649405
            div8 |  -.2135832   .1318624    -1.62   0.105    -.4720288    .0448624
            div9 |  -.2643419   .1227428    -2.15   0.031    -.5049133   -.0237705
elevat_range_msa |  -.0483953   .0565887    -0.86   0.392    -.1593072    .0625166
  ruggedness_msa |    7.88871   2.494976     3.16   0.002     2.998647    12.77877
      heating_dd |  -.0085338   .0034228    -2.49   0.013    -.0152423   -.0018253
      cooling_dd |  -.0101751   .0095016    -1.07   0.284    -.0287979    .0084477
          sprawl |  -.0001162   .0027476    -0.04   0.966    -.0055014    .0052689
        _Iyear_2 |   .3683634   .0412271     8.93   0.000     .2875598     .449167
        _Iyear_3 |   .5855048    .044486    13.16   0.000     .4983138    .6726958
           _cons |   4.853305   .5674855     8.55   0.000     3.741054    5.965556
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .7988999   .0701081    11.40   0.000     .6614906    .9363092
           l_pop |   .4218508   .0623096     6.77   0.000     .2997262    .5439754
            div2 |  -.3507536   .0613933    -5.71   0.000    -.4710823   -.2304249
            div3 |   -.039395   .0572117    -0.69   0.491    -.1515278    .0727378
            div4 |  -.1095879   .0898196    -1.22   0.222    -.2856311    .0664553
            div5 |  -.1741807   .0939346    -1.85   0.064    -.3582892    .0099278
            div6 |  -.2638738   .0884505    -2.98   0.003    -.4372337   -.0905139
            div7 |  -.2400817   .1099228    -2.18   0.029    -.4555264    -.024637
            div8 |  -.2066757   .1006512    -2.05   0.040    -.4039484   -.0094029
            div9 |  -.2781476   .0915135    -3.04   0.002    -.4575107   -.0987846
elevat_range_msa |  -.0156364   .0390938    -0.40   0.689    -.0922589    .0609861
  ruggedness_msa |   5.515807   1.669772     3.30   0.001     2.243114    8.788501
      heating_dd |  -.0047857    .002432    -1.97   0.049    -.0095523   -.0000191
      cooling_dd |   .0047746   .0063337     0.75   0.451    -.0076393    .0171884
          sprawl |   .0027846   .0021122     1.32   0.187    -.0013551    .0069244
        _Iyear_2 |   .3278949   .0295632    11.09   0.000      .269952    .3858377
        _Iyear_3 |   .5509189   .0324509    16.98   0.000     .4873163    .6145216
           _cons |   4.824475   .5047552     9.56   0.000     3.835173    5.813777
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898 l_hwy1947 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           14.227                 2.484
Constant effect |            3.645                 2.027
Dominance       |            0.000                 2.332
Exogeneity      |            2.500                 2.482
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           14.227                 2.247
Constant effect |            3.645                 1.815
Dominance       |            0.000                 2.030
Exogeneity      |            2.500                 2.268
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat coefplot, name (ivqr_pooled_panel_1_model_3, replace) title(.
> )  ytitle(Elasticity of VMT to Interstate Highway Capacity) xtitle(0th Quantile to 
> 100th Quantile: Low Initial Congestion to High Initial Congestion) note(Note: The g
> raph is analogous to Model 3 in the first panel of Table 2)

.                 
.                 graph export "/Users/abhram/Library/CloudStorage/Dropbox/Research/t
> rA/replication/pooled_ivqr_model3.pdf", replace
file
    /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/pooled_ivqr
    > _model3.pdf saved as PDF format

. 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln = l_rail1898 l_hwy1947 l_pix1835), robust    
>                        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   13371.40
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9488
                                                  Root MSE        =     .30994

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.008793   .0774837    13.02   0.000     .8569279    1.160659
           l_pop |   .3435467   .0616805     5.57   0.000     .2226551    .4644382
   S_somecollege |   1.049689   .2239771     4.69   0.000     .6107024    1.488676
   l_mean_income |  -.7355972   .2101318    -3.50   0.000    -1.147448   -.3237464
          S_poor |  -.5171109   .3527822    -1.47   0.143    -1.208551    .1743296
         S_manuf |    .524865   .2536321     2.07   0.039     .0277552    1.021975
            div2 |  -.1236505   .0700403    -1.77   0.077    -.2609269     .013626
            div3 |   .1073522   .0852717     1.26   0.208    -.0597772    .2744816
            div4 |  -.0412027   .0921461    -0.45   0.655    -.2218056    .1394003
            div5 |  -.0848637   .0872127    -0.97   0.331    -.2557975      .08607
            div6 |  -.1791086   .0977478    -1.83   0.067    -.3706909    .0124736
            div7 |  -.0732386   .1063821    -0.69   0.491    -.2817437    .1352665
            div8 |  -.3063858    .118339    -2.59   0.010     -.538326   -.0744455
            div9 |  -.2378768   .1118742    -2.13   0.033    -.4571463   -.0186073
elevat_range_msa |  -.0512695    .031601    -1.62   0.105    -.1132063    .0106674
  ruggedness_msa |   6.790079   1.922884     3.53   0.000     3.021296    10.55886
      heating_dd |  -.0136714   .0025939    -5.27   0.000    -.0187552   -.0085875
      cooling_dd |  -.0184901   .0056436    -3.28   0.001    -.0295514   -.0074288
          sprawl |   .0021733   .0018532     1.17   0.241     -.001459    .0058055
        _Iyear_2 |   .2757887   .0491071     5.62   0.000     .1795405    .3720369
        _Iyear_3 |   .3919424   .0882701     4.44   0.000     .2189363    .5649486
           _cons |   11.72303   1.932125     6.07   0.000     7.936138    15.50993
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln = l_rail1898 l_hwy1947 l_pix1835) ,  quantile
> (10 25 50 75 90)   vce(robust)        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(105) = 34745.52
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.470603   .1421699    10.34   0.000     1.191955    1.749251
           l_pop |   .0690712   .1076589     0.64   0.521    -.1419363    .2800788
   S_somecollege |   .9738442   .4528007     2.15   0.031     .0863712    1.861317
   l_mean_income |  -1.036385   .4031684    -2.57   0.010     -1.82658   -.2461892
          S_poor |  -.7243394   .6735642    -1.08   0.282    -2.044501    .5958223
         S_manuf |    .662758   .3223936     2.06   0.040     .0308782    1.294638
            div2 |   .2657593   .4627458     0.57   0.566    -.6412057    1.172724
            div3 |    .467255   .4836466     0.97   0.334    -.4806748    1.415185
            div4 |   .2332366   .4645699     0.50   0.616    -.6773037    1.143777
            div5 |   .3556988   .5142642     0.69   0.489    -.6522406    1.363638
            div6 |   .2539702   .4994278     0.51   0.611    -.7248904    1.232831
            div7 |   .2355899   .5306059     0.44   0.657    -.8043786    1.275558
            div8 |  -.2996175   .5204682    -0.58   0.565    -1.319716    .7204815
            div9 |   .3018418   .6516118     0.46   0.643    -.9752939    1.578978
elevat_range_msa |  -.2095778   .0712904    -2.94   0.003    -.3493045   -.0698512
  ruggedness_msa |   17.21795   3.894108     4.42   0.000     9.585637    24.85026
      heating_dd |  -.0133284   .0054897    -2.43   0.015     -.024088   -.0025687
      cooling_dd |    -.01539   .0098119    -1.57   0.117    -.0346209    .0038409
          sprawl |   .0028894   .0038696     0.75   0.455    -.0046948    .0104736
        _Iyear_2 |   .2871775   .0780056     3.68   0.000     .1342894    .4400656
        _Iyear_3 |    .403292   .1422489     2.84   0.005     .1244893    .6820946
           _cons |   14.29926   3.478826     4.11   0.000     7.480888    21.11763
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.213825   .1195862    10.15   0.000     .9794405     1.44821
           l_pop |   .1766831   .0850327     2.08   0.038     .0100222    .3433441
   S_somecollege |   .9763554   .3749386     2.60   0.009     .2414893    1.711222
   l_mean_income |  -.8789862   .3281671    -2.68   0.007    -1.522182   -.2357905
          S_poor |  -.8978285   .6080713    -1.48   0.140    -2.089626    .2939694
         S_manuf |   .7470916   .3319319     2.25   0.024      .096517    1.397666
            div2 |  -.1346905   .1613468    -0.83   0.404    -.4509245    .1815434
            div3 |   .0740414   .1721324     0.43   0.667    -.2633318    .4114146
            div4 |  -.1128193   .1715693    -0.66   0.511    -.4490889    .2234504
            div5 |  -.2001646   .1737094    -1.15   0.249    -.5406287    .1402996
            div6 |  -.2454271   .1806547    -1.36   0.174    -.5995038    .1086496
            div7 |  -.1313511    .197299    -0.67   0.506    -.5180501    .2553479
            div8 |  -.7089436   .2046063    -3.46   0.001    -1.109965   -.3079227
            div9 |  -.3262492   .2134855    -1.53   0.126    -.7446731    .0921748
elevat_range_msa |  -.0603259   .0444155    -1.36   0.174    -.1473788    .0267269
  ruggedness_msa |   10.46268   2.278424     4.59   0.000     5.997054    14.92831
      heating_dd |  -.0180209   .0033722    -5.34   0.000    -.0246303   -.0114115
      cooling_dd |  -.0245035   .0081983    -2.99   0.003    -.0405719   -.0084352
          sprawl |   .0006155   .0023734     0.26   0.795    -.0040363    .0052674
        _Iyear_2 |   .3469013    .089496     3.88   0.000     .1714923    .5223103
        _Iyear_3 |   .4371883   .1578231     2.77   0.006     .1278606    .7465159
           _cons |   14.21597   3.081513     4.61   0.000     8.176319    20.25563
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9037975   .0960238     9.41   0.000     .7155943    1.092001
           l_pop |   .3689708   .0849207     4.34   0.000     .2025293    .5354123
   S_somecollege |     .85311   .2637224     3.23   0.001     .3362236    1.369996
   l_mean_income |  -.6489916   .2344538    -2.77   0.006    -1.108513   -.1894706
          S_poor |  -.4949706   .4861888    -1.02   0.309    -1.447883    .4579418
         S_manuf |   .0309538   .2965537     0.10   0.917    -.5502808    .6121883
            div2 |  -.0775559   .1388171    -0.56   0.576    -.3496324    .1945206
            div3 |   .2440331   .1653512     1.48   0.140    -.0800493    .5681154
            div4 |   .0306798    .178585     0.17   0.864    -.3193404    .3807001
            div5 |  -.0213848   .1676134    -0.13   0.898    -.3499009    .3071314
            div6 |  -.0496696   .1862735    -0.27   0.790    -.4147588    .3154197
            div7 |   .0424404   .1887614     0.22   0.822    -.3275252    .4124059
            div8 |  -.2428829   .2212816    -1.10   0.272    -.6765868     .190821
            div9 |  -.2697476    .210919    -1.28   0.201    -.6831412     .143646
elevat_range_msa |  -.0373156   .0512751    -0.73   0.467    -.1378129    .0631818
  ruggedness_msa |   7.542961   1.993747     3.78   0.000      3.63529    11.45063
      heating_dd |  -.0174941   .0036399    -4.81   0.000    -.0246282     -.01036
      cooling_dd |  -.0295603   .0074451    -3.97   0.000    -.0441525   -.0149682
          sprawl |   .0006601   .0030363     0.22   0.828    -.0052909    .0066111
        _Iyear_2 |   .2650282   .0713065     3.72   0.000       .12527    .4047863
        _Iyear_3 |   .3904616   .1283009     3.04   0.002     .1389964    .6419267
           _cons |   11.69283   1.948532     6.00   0.000      7.87378    15.51188
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8326664   .0613961    13.56   0.000     .7123323    .9530005
           l_pop |   .4055338   .0446202     9.09   0.000     .3180797    .4929878
   S_somecollege |   .2529827   .2217898     1.14   0.254    -.1817173    .6876827
   l_mean_income |  -.1168866   .2001441    -0.58   0.559    -.5091618    .2753886
          S_poor |  -.2626171   .3856636    -0.68   0.496    -1.018504    .4932696
         S_manuf |  -.2182351   .2431924    -0.90   0.370    -.6948834    .2584132
            div2 |   -.290272   .0803407    -3.61   0.000     -.447737   -.1328071
            div3 |   .0056693   .0843217     0.07   0.946    -.1595982    .1709368
            div4 |   -.166097   .0855116    -1.94   0.052    -.3336966    .0015026
            div5 |  -.0856373   .0930695    -0.92   0.357    -.2680502    .0967756
            div6 |  -.1074518   .0923442    -1.16   0.245    -.2884431    .0735394
            div7 |  -.0856358   .0984368    -0.87   0.384    -.2785685    .1072968
            div8 |  -.2024025   .1490811    -1.36   0.175     -.494596     .089791
            div9 |   -.230175   .1647474    -1.40   0.162    -.5530739    .0927239
elevat_range_msa |  -.0694586    .050854    -1.37   0.172    -.1691306    .0302134
  ruggedness_msa |   7.461848   1.967876     3.79   0.000     3.604881    11.31881
      heating_dd |    -.00944   .0036558    -2.58   0.010    -.0166054   -.0022747
      cooling_dd |  -.0144129   .0078545    -1.83   0.067    -.0298074    .0009816
          sprawl |    .000771   .0015803     0.49   0.626    -.0023264    .0038684
        _Iyear_2 |     .29992   .0572846     5.24   0.000     .1876443    .4121957
        _Iyear_3 |    .463648   .0994085     4.66   0.000     .2688108    .6584851
           _cons |   6.314647   1.825884     3.46   0.001     2.735981    9.893313
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .7438754   .0988631     7.52   0.000     .5501072    .9376436
           l_pop |   .4293039   .0802776     5.35   0.000     .2719627    .5866451
   S_somecollege |   1.200984   .2946406     4.08   0.000     .6234987    1.778469
   l_mean_income |   .0185082   .2732504     0.07   0.946    -.5170526    .5540691
          S_poor |  -.6910883   .4872227    -1.42   0.156    -1.646027    .2638507
         S_manuf |   .2505445   .4722872     0.53   0.596    -.6751214     1.17621
            div2 |  -.2361821   .0845646    -2.79   0.005    -.4019257   -.0704384
            div3 |   .0437267   .0820491     0.53   0.594    -.1170867      .20454
            div4 |  -.1086987   .1064405    -1.02   0.307    -.3173183    .0999209
            div5 |  -.1073407   .1298054    -0.83   0.408    -.3617546    .1470731
            div6 |  -.1488263   .1236501    -1.20   0.229     -.391176    .0935234
            div7 |   -.163176   .1302937    -1.25   0.210     -.418547     .092195
            div8 |  -.2213346   .1330669    -1.66   0.096    -.4821409    .0394717
            div9 |   -.316553   .1060551    -2.98   0.003    -.5244172   -.1086888
elevat_range_msa |  -.0182482   .0448848    -0.41   0.684    -.1062209    .0697245
  ruggedness_msa |   4.539105   1.671063     2.72   0.007     1.263882    7.814327
      heating_dd |  -.0070144   .0030935    -2.27   0.023    -.0130776   -.0009513
      cooling_dd |   .0003393   .0096151     0.04   0.972    -.0185059    .0191846
          sprawl |    .004143   .0021877     1.89   0.058    -.0001448    .0084307
        _Iyear_2 |   .1568789   .0713489     2.20   0.028     .0170377    .2967201
        _Iyear_3 |   .2119001   .1364851     1.55   0.121    -.0556058     .479406
           _cons |   4.745837   2.669593     1.78   0.075    -.4864704    9.978143
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           13.836                 2.600
Constant effect |            4.645                 2.154
Dominance       |            0.000                 2.302
Exogeneity      |            3.283                 2.683
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           13.836                 2.304
Constant effect |            4.645                 1.888
Dominance       |            0.000                 2.237
Exogeneity      |            3.283                 2.270
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat coefplot, name (ivqr_pooled_panel_1_model_4, replace) subtitl
> e (Model 4)

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln = l_rail1898 l_hwy1947 l_pix1835), robust                               
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   14112.87
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9497
                                                  Root MSE        =     .30692

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.039813   .0844881    12.31   0.000     .8742196    1.205407
           l_pop |    .233254   .1309338     1.78   0.075    -.0233716    .4898796
         l_pop80 |    .674398   .2878259     2.34   0.019     .1102695    1.238526
         l_pop70 |  -.1888125   .3516301    -0.54   0.591    -.8779949    .5003699
         l_pop60 |  -.5019758   .2773509    -1.81   0.070    -1.045574     .041622
         l_pop50 |  -.0334606    .175969    -0.19   0.849    -.3783534    .3114323
         l_pop40 |   .0054055   .1924353     0.03   0.978    -.3717607    .3825717
         l_pop30 |   .1401975   .1646682     0.85   0.395    -.1825462    .4629411
         l_pop20 |  -.0068555   .0806719    -0.08   0.932    -.1649695    .1512585
   S_somecollege |   .5910127   .2847117     2.08   0.038     .0329881    1.149037
   l_mean_income |  -.5940658   .2103193    -2.82   0.005    -1.006284   -.1818476
          S_poor |  -.3219207    .355494    -0.91   0.365    -1.018676    .3748347
         S_manuf |   .4799153   .2464645     1.95   0.052    -.0031463    .9629768
            div2 |  -.1404319   .0718841    -1.95   0.051    -.2813222    .0004583
            div3 |   .0770117   .0899273     0.86   0.392    -.0992425     .253266
            div4 |  -.0409546   .0923378    -0.44   0.657    -.2219333    .1400241
            div5 |  -.1245138   .0911048    -1.37   0.172     -.303076    .0540484
            div6 |    -.20477   .1003513    -2.04   0.041     -.401455    -.008085
            div7 |  -.0685621   .1081257    -0.63   0.526    -.2804846    .1433604
            div8 |    -.38036   .1405709    -2.71   0.007    -.6558738   -.1048461
            div9 |   -.268726   .1336834    -2.01   0.044    -.5307407   -.0067114
elevat_range_msa |  -.0580455   .0311163    -1.87   0.062    -.1190323    .0029414
  ruggedness_msa |   5.103778   1.925562     2.65   0.008     1.329746     8.87781
      heating_dd |  -.0157404   .0027467    -5.73   0.000    -.0211238    -.010357
      cooling_dd |  -.0286064   .0063494    -4.51   0.000    -.0410509   -.0161619
          sprawl |    .001145   .0020114     0.57   0.569    -.0027973    .0050873
        _Iyear_2 |     .34237   .0543143     6.30   0.000      .235916    .4488241
        _Iyear_3 |   .5292628   .1020702     5.19   0.000     .3292089    .7293167
           _cons |   10.73986   1.923733     5.58   0.000     6.969411    14.51031
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln = l_rail1898 l_hwy1947 l_pix1835) ,  quantile(10 25 50 75 90) vce(robust
> )  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(140) = 33724.97
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.334491   .1788024     7.46   0.000      .984045    1.684938
           l_pop |   .3133952   .2076083     1.51   0.131    -.0935096    .7203001
         l_pop80 |   .1878852   .6270338     0.30   0.764    -1.041079    1.416849
         l_pop70 |   .2799914   .8978415     0.31   0.755    -1.479746    2.039728
         l_pop60 |  -.8704461    .880055    -0.99   0.323    -2.595322    .8544299
         l_pop50 |   .0271834   .6143268     0.04   0.965    -1.176875    1.231242
         l_pop40 |    .427127   .4413963     0.97   0.333     -.437994    1.292248
         l_pop30 |  -.1662574   .5118862    -0.32   0.745    -1.169536    .8370211
         l_pop20 |  -.0815564   .2125992    -0.38   0.701    -.4982432    .3351304
   S_somecollege |    .899222    .874995     1.03   0.304    -.8157367    2.614181
   l_mean_income |  -.9363525   .7520277    -1.25   0.213      -2.4103    .5375947
          S_poor |  -.6244656   .7650249    -0.82   0.414    -2.123887    .8749556
         S_manuf |   .8034738   .5050254     1.59   0.112    -.1863578    1.793306
            div2 |   .2255304   .4130329     0.55   0.585    -.5839992     1.03506
            div3 |   .3454437   .4542862     0.76   0.447    -.5449408    1.235828
            div4 |   .2325306   .4036002     0.58   0.565    -.5585112    1.023572
            div5 |   .1577876   .4842943     0.33   0.745    -.7914118    1.106987
            div6 |    .174598   .4745152     0.37   0.713    -.7554347    1.104631
            div7 |   .1036348   .5088439     0.20   0.839     -.893681    1.100951
            div8 |   -.362028   .5427008    -0.67   0.505    -1.425702    .7016461
            div9 |   .2189774   .6964829     0.31   0.753    -1.146104    1.584059
elevat_range_msa |  -.1627905   .1137035    -1.43   0.152    -.3856453    .0600642
  ruggedness_msa |   8.580226   8.253169     1.04   0.299    -7.595687    24.75614
      heating_dd |  -.0156016   .0075021    -2.08   0.038    -.0303055   -.0008978
      cooling_dd |  -.0279317    .016535    -1.69   0.091    -.0603396    .0044763
          sprawl |   .0026717   .0064232     0.42   0.677    -.0099175    .0152608
        _Iyear_2 |   .2688213   .1348976     1.99   0.046     .0044268    .5332158
        _Iyear_3 |   .4077684   .2287245     1.78   0.075    -.0405233    .8560601
           _cons |   13.85644   6.278168     2.21   0.027     1.551458    26.16143
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.200551   .0780467    15.38   0.000     1.047583     1.35352
           l_pop |    .220562   .1600876     1.38   0.168    -.0932039    .5343279
         l_pop80 |   .5648934   .3297741     1.71   0.087     -.081452    1.211239
         l_pop70 |  -.0255241   .3319656    -0.08   0.939    -.6761647    .6251165
         l_pop60 |  -.8454487   .4045561    -2.09   0.037    -1.638364   -.0525332
         l_pop50 |   .0980883   .2972862     0.33   0.741     -.484582    .6807587
         l_pop40 |   .1886376   .2003045     0.94   0.346    -.2039521    .5812273
         l_pop30 |  -.0566766   .1454054    -0.39   0.697     -.341666    .2283128
         l_pop20 |   .0459436   .0794373     0.58   0.563    -.1097506    .2016379
   S_somecollege |   .3600703   .3617777     1.00   0.320     -.349001    1.069142
   l_mean_income |  -.4317197   .2701629    -1.60   0.110    -.9612292    .0977898
          S_poor |  -.6437416   .4925543    -1.31   0.191     -1.60913    .3216471
         S_manuf |   .4942475   .2177396     2.27   0.023     .0674857    .9210093
            div2 |  -.0658252   .1457211    -0.45   0.651    -.3514333    .2197829
            div3 |   .1223328   .1551155     0.79   0.430     -.181688    .4263536
            div4 |  -.0093326   .1567119    -0.06   0.953    -.3164822     .297817
            div5 |  -.1436184   .1609911    -0.89   0.372    -.4591552    .1719185
            div6 |  -.2070009   .1646014    -1.26   0.209    -.5296137    .1156119
            div7 |   -.079372   .1829905    -0.43   0.664    -.4380267    .2792827
            div8 |  -.6972702   .2001782    -3.48   0.000    -1.089612    -.304928
            div9 |  -.3264004   .2359371    -1.38   0.167    -.7888286    .1360279
elevat_range_msa |  -.0616268   .0405022    -1.52   0.128    -.1410096     .017756
  ruggedness_msa |   9.240651   2.145536     4.31   0.000     5.035476    13.44582
      heating_dd |  -.0209447   .0032247    -6.50   0.000    -.0272651   -.0146243
      cooling_dd |     -.0342    .007636    -4.48   0.000    -.0491662   -.0192338
          sprawl |   .0015768   .0024364     0.65   0.517    -.0031984     .006352
        _Iyear_2 |   .3716632   .0668495     5.56   0.000     .2406406    .5026859
        _Iyear_3 |   .5166856   .1219669     4.24   0.000     .2776348    .7557364
           _cons |   10.03142   2.524224     3.97   0.000     5.084028    14.97881
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   1.032256   .1143739     9.03   0.000     .8080877    1.256425
           l_pop |   .2200336   .1705187     1.29   0.197    -.1141768     .554244
         l_pop80 |     .52188   .4200758     1.24   0.214    -.3014534    1.345213
         l_pop70 |   .1266682   .5131623     0.25   0.805    -.8791114    1.132448
         l_pop60 |  -.7381377   .4419927    -1.67   0.095    -1.604427    .1281521
         l_pop50 |   .1318594   .2177424     0.61   0.545    -.2949078    .5586267
         l_pop40 |  -.0237895   .2496652    -0.10   0.924    -.5131243    .4655452
         l_pop30 |   .0218607   .2832253     0.08   0.938    -.5332507    .5769722
         l_pop20 |   .0256615   .2380307     0.11   0.914    -.4408702    .4921931
   S_somecollege |   .0805155   .4001016     0.20   0.841    -.7036692    .8647002
   l_mean_income |  -.5808011   .2848636    -2.04   0.041    -1.139124   -.0224786
          S_poor |  -.2768661   .4739581    -0.58   0.559    -1.205807    .6520747
         S_manuf |    .083864   .3597452     0.23   0.816    -.6212237    .7889516
            div2 |  -.2537977   .0756044    -3.36   0.001    -.4019797   -.1056157
            div3 |   .0249951   .0841759     0.30   0.767    -.1399866    .1899768
            div4 |  -.1517329    .114336    -1.33   0.184    -.3758274    .0723615
            div5 |  -.2543571   .1060819    -2.40   0.016    -.4622739   -.0464403
            div6 |  -.2310374   .1169277    -1.98   0.048    -.4602115   -.0018633
            div7 |  -.2076481   .1210287    -1.72   0.086      -.44486    .0295637
            div8 |  -.5968886   .2197644    -2.72   0.007    -1.027619   -.1661582
            div9 |  -.5794282    .198847    -2.91   0.004    -.9691612   -.1896952
elevat_range_msa |  -.0047703   .0516435    -0.09   0.926    -.1059897    .0964491
  ruggedness_msa |   5.672359   2.128845     2.66   0.008       1.4999    9.844817
      heating_dd |  -.0207294   .0041186    -5.03   0.000    -.0288016   -.0126572
      cooling_dd |  -.0414125   .0093222    -4.44   0.000    -.0596837   -.0231412
          sprawl |  -.0027532   .0032702    -0.84   0.400    -.0091626    .0036562
        _Iyear_2 |   .3652539   .0790551     4.62   0.000     .2103086    .5201991
        _Iyear_3 |   .5890528   .1465272     4.02   0.000     .3018647    .8762409
           _cons |   12.06702   2.600919     4.64   0.000     6.969316    17.16473
-----------------+----------------------------------------------------------------
q75              |
            l_ln |    .825758    .076924    10.73   0.000     .6749899    .9765262
           l_pop |   .2696171   .1523869     1.77   0.077    -.0290558      .56829
         l_pop80 |   .4244966   .3837698     1.11   0.269    -.3276784    1.176672
         l_pop70 |  -.0690586   .4477203    -0.15   0.877    -.9465742    .8084571
         l_pop60 |  -.2943558   .3667292    -0.80   0.422    -1.013132    .4244202
         l_pop50 |   -.154275   .2068241    -0.75   0.456    -.5596428    .2510929
         l_pop40 |   .1371234     .21735     0.63   0.528    -.2888747    .5631216
         l_pop30 |   .1238483     .23045     0.54   0.591    -.3278253    .5755219
         l_pop20 |  -.0291796   .1385887    -0.21   0.833    -.3008085    .2424493
   S_somecollege |   .4012841   .3428237     1.17   0.242    -.2706381    1.073206
   l_mean_income |  -.0417773   .2183007    -0.19   0.848    -.4696387    .3860842
          S_poor |   .0208652   .4241816     0.05   0.961    -.8105154    .8522458
         S_manuf |    -.21637   .2676233    -0.81   0.419     -.740902    .3081619
            div2 |  -.2725957   .0781365    -3.49   0.000    -.4257404   -.1194511
            div3 |   .0110681   .0839369     0.13   0.895    -.1534453    .1755815
            div4 |  -.1638803   .0889511    -1.84   0.065    -.3382213    .0104607
            div5 |  -.1060705   .0939943    -1.13   0.259    -.2902959    .0781549
            div6 |  -.1587088   .0947077    -1.68   0.094    -.3443326     .026915
            div7 |  -.1301005   .1062063    -1.22   0.221    -.3382609    .0780599
            div8 |  -.1814465    .180937    -1.00   0.316    -.5360765    .1731835
            div9 |   -.213147   .1863912    -1.14   0.253    -.5784671    .1521731
elevat_range_msa |  -.0744123   .0530059    -1.40   0.160    -.1783021    .0294774
  ruggedness_msa |   5.405475   1.969896     2.74   0.006      1.54455    9.266401
      heating_dd |  -.0117209   .0039949    -2.93   0.003    -.0195508   -.0038911
      cooling_dd |  -.0185383   .0090533    -2.05   0.041    -.0362824   -.0007941
          sprawl |   .0012516   .0020509     0.61   0.542    -.0027681    .0052713
        _Iyear_2 |   .3071999   .0692363     4.44   0.000     .1714993    .4429006
        _Iyear_3 |    .497252   .1279055     3.89   0.000     .2465618    .7479423
           _cons |   5.637832   2.062807     2.73   0.006     1.594804     9.68086
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .7077955    .126557     5.59   0.000     .4597484    .9558426
           l_pop |   .4355482   .2175862     2.00   0.045      .009087    .8620094
         l_pop80 |   .0503922    .541198     0.09   0.926    -1.010336    1.111121
         l_pop70 |   .3960718   .7331677     0.54   0.589     -1.04091    1.833054
         l_pop60 |  -.5352883   .4718186    -1.13   0.257    -1.460036    .3894592
         l_pop50 |   .0587446   .4466722     0.13   0.895    -.8167167     .934206
         l_pop40 |   -.110906   .3559387    -0.31   0.755    -.8085331     .586721
         l_pop30 |   .1722234   .2491674     0.69   0.489    -.3161357    .6605824
         l_pop20 |   .0093624   .1689356     0.06   0.956    -.3217454    .3404702
   S_somecollege |   .5536994   .4880603     1.13   0.257    -.4028813     1.51028
   l_mean_income |   .0235908   .3090337     0.08   0.939    -.5821041    .6292857
          S_poor |  -.5322696   .5023128    -1.06   0.289    -1.516785    .4522454
         S_manuf |   .0231329   .5104262     0.05   0.964    -.9772841     1.02355
            div2 |  -.2066682   .0728294    -2.84   0.005    -.3494111   -.0639253
            div3 |   .0606026    .079971     0.76   0.449    -.0961377    .2173429
            div4 |  -.0674497   .1039635    -0.65   0.516    -.2712145     .136315
            div5 |  -.1290672   .1051706    -1.23   0.220    -.3351977    .0770633
            div6 |  -.1710305   .0979492    -1.75   0.081    -.3630073    .0209464
            div7 |  -.1463351   .1125612    -1.30   0.194    -.3669509    .0742808
            div8 |  -.1089632    .164628    -0.66   0.508    -.4316282    .2137017
            div9 |    -.18109   .1325637    -1.37   0.172    -.4409101    .0787301
elevat_range_msa |  -.0298585   .0462854    -0.65   0.519    -.1205761    .0608592
  ruggedness_msa |   4.785426   2.559162     1.87   0.061    -.2304397    9.801292
      heating_dd |  -.0068798   .0041358    -1.66   0.096    -.0149859    .0012262
      cooling_dd |   .0030898   .0119729     0.26   0.796    -.0203766    .0265563
          sprawl |   .0048268   .0023663     2.04   0.041      .000189    .0094647
        _Iyear_2 |   .2513771   .0879423     2.86   0.004     .0790134    .4237408
        _Iyear_3 |   .3594921   .1689307     2.13   0.033     .0283941    .6905902
           _cons |   4.431361   2.899287     1.53   0.126    -1.251136    10.11386
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           12.537                 2.303
Constant effect |            3.703                 2.279
Dominance       |            0.000                 2.222
Exogeneity      |            3.335                 2.525
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           12.537                 2.103
Constant effect |            3.703                 1.961
Dominance       |            0.000                 2.055
Exogeneity      |            3.335                 2.238
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat coefplot, name (ivqr_pooled_panel_1_model_5, replace) subtitl
> e (Model 5)

.                 
.                 
. 
.                 *graph combine ivqr_pooled_panel_1_model_1 ivqr_pooled_panel_1_mode
> l_2 ivqr_pooled_panel_1_model_3 ivqr_pooled_panel_1_model_4 ivqr_pooled_panel_1_mod
> el_5, xcommon ycommon altshrink title("Pooled IVQR estimates of elasticity of aggre
> gate MSA VMT to IH lane miles") note("Note: The graphs are analogous to the first p
> anel of Table 2 ") 
.                 
.                 *graph export "/Users/abhram/Library/CloudStorage/Dropbox/Research/
> trA/replication/pooled_ivqr.pdf", replace    
. 
.         
.         
.         
. * Second Panel *        
.                 
.                 local Inst "l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_hwy1947 ), robust    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    2626.59
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8726
                                                  Root MSE        =     .48861

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.333793    .028953    46.07   0.000     1.277046     1.39054
    _Iyear_2 |   .3973409   .0473145     8.40   0.000     .3046062    .4900756
    _Iyear_3 |   .6626131   .0467988    14.16   0.000     .5708892     .754337
       _cons |   6.164348   .1888629    32.64   0.000     5.794184    6.534513
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 xi: ivqregress iqr l_vmt i.year (l_ln =  l_hwy1947 ) ,  quantile(10
>  25 50 75 90) vce(robust)    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     684
Estimator: Inverse quantile regression                 Wald chi2(15) = 4498.14
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.477275   .0407714    36.23   0.000     1.397365    1.557185
    _Iyear_2 |    .352804   .1290593     2.73   0.006     .0998524    .6057556
    _Iyear_3 |   .6835127   .1139158     6.00   0.000     .4602418    .9067836
       _cons |   4.594233   .3111813    14.76   0.000     3.984329    5.204137
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.411656   .0282124    50.04   0.000      1.35636    1.466951
    _Iyear_2 |   .4436853   .0531779     8.34   0.000     .3394585    .5479121
    _Iyear_3 |   .7070536   .0539515    13.11   0.000     .6013106    .8127965
       _cons |   5.385584   .1990572    27.06   0.000     4.995439    5.775729
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.297083   .0232277    55.84   0.000     1.251558    1.342609
    _Iyear_2 |   .4370945   .0444396     9.84   0.000     .3499945    .5241945
    _Iyear_3 |   .6697732   .0465836    14.38   0.000      .578471    .7610753
       _cons |   6.474416   .1644537    39.37   0.000     6.152093     6.79674
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.268065   .0289477    43.81   0.000     1.211329    1.324802
    _Iyear_2 |   .3634815   .0493514     7.37   0.000     .2667544    .4602085
    _Iyear_3 |   .6468601   .0480555    13.46   0.000     .5526731    .7410472
       _cons |    6.91221    .202239    34.18   0.000     6.515829    7.308591
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.243359   .0476169    26.11   0.000     1.150032    1.336687
    _Iyear_2 |   .3976081    .059908     6.64   0.000     .2801907    .5150255
    _Iyear_3 |   .5781507   .0540691    10.69   0.000     .4721773    .6841242
       _cons |   7.292977   .3192766    22.84   0.000     6.667206    7.918748
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           55.842                 2.195
Constant effect |            4.226                 2.417
Dominance       |            0.000                 2.374
Exogeneity      |            2.631                 2.306
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           55.842                 1.989
Constant effect |            4.226                 2.046
Dominance       |            0.000                 1.957
Exogeneity      |            2.631                 2.047
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln =  l_hwy1947 ), robust
>                      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7210.35
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9295
                                                  Root MSE        =     .36354

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9999055    .069237    14.44   0.000     .8642035    1.135607
       l_pop |   .3519581   .0516779     6.81   0.000     .2506713    .4532449
    _Iyear_2 |   .3928344   .0347872    11.29   0.000     .3246528    .4610161
    _Iyear_3 |     .63034   .0346843    18.17   0.000     .5623599      .69832
       _cons |   3.855266   .2554794    15.09   0.000     3.354536    4.355996
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_hwy1947

. 
.                 xi: ivqregress iqr l_vmt l_pop i.year (l_ln =  l_hwy1947 ) ,  quant
> ile(10 25 50 75 90) vce(robust)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(20) = 11707.68
                                                      Prob > chi2   =   0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.055788   .0630406    16.75   0.000      .932231    1.179346
       l_pop |   .3858828   .0436464     8.84   0.000     .3003375    .4714282
    _Iyear_2 |   .3347399   .0607977     5.51   0.000     .2155786    .4539012
    _Iyear_3 |   .5557646   .0496002    11.20   0.000       .45855    .6529792
       _cons |   2.705002   .2086513    12.96   0.000     2.296053    3.113951
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.066302   .0814964    13.08   0.000     .9065724    1.226032
       l_pop |   .3096526    .061139     5.06   0.000     .1898225    .4294828
    _Iyear_2 |   .4834823   .0445019    10.86   0.000     .3962603    .5707044
    _Iyear_3 |   .6879603   .0466872    14.74   0.000      .596455    .7794656
       _cons |   3.712211   .2975604    12.48   0.000     3.129004    4.295419
-------------+----------------------------------------------------------------
q50          |
        l_ln |    1.07499   .0940999    11.42   0.000     .8905579    1.259423
       l_pop |   .2563202   .0797534     3.21   0.001     .1000064     .412634
    _Iyear_2 |   .3915186   .0424742     9.22   0.000     .3082707    .4747666
    _Iyear_3 |   .6336195   .0447604    14.16   0.000     .5458907    .7213483
       _cons |   4.628209   .4482275    10.33   0.000     3.749699    5.506719
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .9020493   .1130507     7.98   0.000      .680474    1.123625
       l_pop |   .3547614   .0936532     3.79   0.000     .1712045    .5383182
    _Iyear_2 |   .3878806   .0443292     8.75   0.000     .3009969    .4747643
    _Iyear_3 |   .6158178   .0455358    13.52   0.000     .5265694    .7050662
       _cons |   4.736067   .4897382     9.67   0.000     3.776197    5.695936
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .8614216   .2145468     4.02   0.000     .4409175    1.281926
       l_pop |   .3709465   .1558321     2.38   0.017     .0655211    .6763718
    _Iyear_2 |   .3206372   .0720402     4.45   0.000     .1794411    .4618333
    _Iyear_3 |   .5393832    .078984     6.83   0.000     .3845774    .6941891
       _cons |    5.06783   .6980588     7.26   0.000      3.69966       6.436
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           11.424                 2.651
Constant effect |            1.490                 2.508
Dominance       |            0.000                 2.192
Exogeneity      |            3.160                 2.299
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           11.424                 2.276
Constant effect |            1.490                 2.103
Dominance       |            0.000                 1.996
Exogeneity      |            3.160                 2.035
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _hwy1947 ), robust                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   11308.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9398
                                                  Root MSE        =     .33594

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.102506   .0781378    14.11   0.000     .9493588    1.255653
           l_pop |   .2431158   .0630777     3.85   0.000     .1194858    .3667458
            div2 |  -.1884822   .0687676    -2.74   0.006    -.3232642   -.0537002
            div3 |   .0124763   .0830866     0.15   0.881    -.1503704     .175323
            div4 |  -.0912973   .0976838    -0.93   0.350    -.2827539    .1001594
            div5 |   -.104381   .0935216    -1.12   0.264    -.2876801     .078918
            div6 |   -.220692   .0982829    -2.25   0.025     -.413323    -.028061
            div7 |  -.1797895   .1051725    -1.71   0.087    -.3859239    .0263449
            div8 |  -.4647331   .1352422    -3.44   0.001     -.729803   -.1996633
            div9 |  -.3802147   .1261847    -3.01   0.003    -.6275323   -.1328972
elevat_range_msa |   -.023727   .0366888    -0.65   0.518    -.0956357    .0481817
  ruggedness_msa |   6.894112   2.159781     3.19   0.001     2.661018     11.1272
      heating_dd |  -.0157042   .0028184    -5.57   0.000    -.0212282   -.0101802
      cooling_dd |  -.0237658   .0058227    -4.08   0.000    -.0351781   -.0123535
          sprawl |   .0003782   .0020399     0.19   0.853    -.0036199    .0043763
        _Iyear_2 |   .3940516   .0320932    12.28   0.000       .33115    .4569531
        _Iyear_3 |   .6401744   .0321762    19.90   0.000     .5771102    .7032387
           _cons |   5.727713   .5365113    10.68   0.000      4.67617    6.779255
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_hwy1947

.                 
.                 xi: ivqregress iqr l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year (l_ln =  l_
> hwy1947 ) ,  quantile(10 25 50 75 90) bound(0 4) vce(robust)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(85) = 31389.40
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.416409   .1368786    10.35   0.000     1.148132    1.684686
           l_pop |   .0606683   .1011474     0.60   0.549    -.1375769    .2589136
            div2 |   .2566974   .5363767     0.48   0.632    -.7945816    1.307976
            div3 |   .3213113   .5348937     0.60   0.548     -.727061    1.369684
            div4 |   .2174064   .5190441     0.42   0.675    -.7999014    1.234714
            div5 |   .1946767   .5585924     0.35   0.727    -.9001443    1.289498
            div6 |   .1293459   .5541464     0.23   0.815    -.9567611    1.215453
            div7 |   .1787658    .563513     0.32   0.751    -.9256994    1.283231
            div8 |  -.4340645   .5301832    -0.82   0.413    -1.473204    .6050754
            div9 |   .0989831   .6237669     0.16   0.874    -1.123578    1.321544
elevat_range_msa |  -.1508576    .057433    -2.63   0.009    -.2634241   -.0382911
  ruggedness_msa |   15.03435   2.968912     5.06   0.000     9.215387    20.85331
      heating_dd |  -.0182728   .0043677    -4.18   0.000    -.0268332   -.0097123
      cooling_dd |  -.0256386    .008505    -3.01   0.003    -.0423081   -.0089691
          sprawl |   .0056545   .0029323     1.93   0.054    -.0000927    .0114017
        _Iyear_2 |   .4254022   .0393576    10.81   0.000     .3482628    .5025416
        _Iyear_3 |    .636078   .0421474    15.09   0.000     .5534706    .7186854
           _cons |   5.173226   .9375929     5.52   0.000     3.335577    7.010874
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.230598    .081553    15.09   0.000     1.070757     1.39044
           l_pop |   .1107014    .068932     1.61   0.108    -.0244029    .2458057
            div2 |  -.2091038   .2034799    -1.03   0.304    -.6079171    .1897096
            div3 |   -.025479   .2104231    -0.12   0.904    -.4379008    .3869428
            div4 |  -.1639041   .2056184    -0.80   0.425    -.5669088    .2391006
            div5 |  -.2733337   .2469528    -1.11   0.268    -.7573523    .2106849
            div6 |  -.3472171   .2513756    -1.38   0.167    -.8399042    .1454701
            div7 |  -.2789884   .2698507    -1.03   0.301    -.8078861    .2499092
            div8 |  -.9073032   .2665767    -3.40   0.001    -1.429784   -.3848225
            div9 |  -.5797165   .3038042    -1.91   0.056    -1.175162    .0157288
elevat_range_msa |  -.0299275   .0520918    -0.57   0.566    -.1320256    .0721705
  ruggedness_msa |   11.82761   4.129789     2.86   0.004     3.733369    19.92185
      heating_dd |  -.0229777   .0059735    -3.85   0.000    -.0346855     -.01127
      cooling_dd |  -.0353763   .0110398    -3.20   0.001    -.0570138   -.0137387
          sprawl |  -.0011783   .0039796    -0.30   0.767    -.0089781    .0066216
        _Iyear_2 |   .4109771   .0611711     6.72   0.000     .2910839    .5308704
        _Iyear_3 |   .6517197   .0578835    11.26   0.000       .53827    .7651693
           _cons |   6.890079   .9707258     7.10   0.000     4.987492    8.792667
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9617122   .0990504     9.71   0.000     .7675771    1.155847
           l_pop |   .2959077   .0830328     3.56   0.000     .1331664     .458649
            div2 |  -.2510597    .107654    -2.33   0.020    -.4620578   -.0400617
            div3 |  -.0032001   .1259107    -0.03   0.980    -.2499806    .2435803
            div4 |  -.1517554   .1440625    -1.05   0.292    -.4341127    .1306018
            div5 |   -.203534    .143541    -1.42   0.156    -.4848692    .0778012
            div6 |  -.2308521   .1630093    -1.42   0.157    -.5503444    .0886402
            div7 |  -.2191095   .1521913    -1.44   0.150    -.5173991      .07918
            div8 |  -.4714049   .2299645    -2.05   0.040     -.922127   -.0206828
            div9 |  -.4904858   .2043653    -2.40   0.016    -.8910344   -.0899371
elevat_range_msa |   -.012922   .0480179    -0.27   0.788    -.1070354    .0811913
  ruggedness_msa |   6.112383   3.073579     1.99   0.047     .0882786    12.13649
      heating_dd |  -.0199676   .0038103    -5.24   0.000    -.0274357   -.0124995
      cooling_dd |  -.0336277    .007958    -4.23   0.000    -.0492251   -.0180303
          sprawl |  -.0016743   .0023075    -0.73   0.468    -.0061969    .0028483
        _Iyear_2 |   .4104101   .0355982    11.53   0.000      .340639    .4801813
        _Iyear_3 |   .6673974   .0362855    18.39   0.000     .5962792    .7385157
           _cons |   6.398128   .8056393     7.94   0.000     4.819104    7.977152
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8760999   .2415484     3.63   0.000     .4026737    1.349526
           l_pop |   .4037754    .195123     2.07   0.039     .0213413    .7862094
            div2 |  -.3182871   .1403045    -2.27   0.023    -.5932788   -.0432954
            div3 |    .000916   .1397699     0.01   0.995     -.273028    .2748599
            div4 |    -.14789   .1811143    -0.82   0.414    -.5028675    .2070876
            div5 |   -.101639   .2111306    -0.48   0.630    -.5154474    .3121695
            div6 |  -.1510621   .1792565    -0.84   0.399    -.5023984    .2002742
            div7 |  -.1167526   .2164345    -0.54   0.590    -.5409564    .3074512
            div8 |  -.2028188   .2119021    -0.96   0.338    -.6181393    .2125018
            div9 |  -.2457622   .1829058    -1.34   0.179     -.604251    .1127265
elevat_range_msa |  -.0550486   .0896479    -0.61   0.539    -.2307553    .1206581
  ruggedness_msa |   7.798719    3.95169     1.97   0.048     .0535496    15.54389
      heating_dd |  -.0076269   .0050622    -1.51   0.132    -.0175487    .0022949
      cooling_dd |  -.0073551   .0130618    -0.56   0.573    -.0329557    .0182456
          sprawl |   .0008493   .0049659     0.17   0.864    -.0088836    .0105822
        _Iyear_2 |   .3624089   .0653137     5.55   0.000     .2343965    .4904213
        _Iyear_3 |   .5707896   .0756571     7.54   0.000     .4225045    .7190747
           _cons |   4.887117   1.142256     4.28   0.000     2.648337    7.125897
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8814903   .1187917     7.42   0.000     .6486628    1.114318
           l_pop |   .3453849    .107977     3.20   0.001      .133754    .5570159
            div2 |  -.3312572   .0689846    -4.80   0.000    -.4664646   -.1960498
            div3 |  -.0460445    .062626    -0.74   0.462    -.1687893    .0767002
            div4 |  -.1886764   .1008516    -1.87   0.061    -.3863418     .008989
            div5 |  -.2015656   .0897196    -2.25   0.025    -.3774127   -.0257184
            div6 |  -.2880414    .095258    -3.02   0.002    -.4747436   -.1013392
            div7 |  -.2743463    .109589    -2.50   0.012    -.4891368   -.0595559
            div8 |  -.3352459   .0977566    -3.43   0.001    -.5268453   -.1436464
            div9 |  -.3101413   .0939968    -3.30   0.001    -.4943717    -.125911
elevat_range_msa |   .0039013   .0413448     0.09   0.925     -.077133    .0849356
  ruggedness_msa |   6.564978   1.850998     3.55   0.000     2.937088    10.19287
      heating_dd |  -.0035918   .0025205    -1.43   0.154     -.008532    .0013483
      cooling_dd |   .0078647   .0061826     1.27   0.203    -.0042529    .0199823
          sprawl |   .0000912   .0022762     0.04   0.968    -.0043701    .0045525
        _Iyear_2 |   .3262091   .0315084    10.35   0.000     .2644538    .3879644
        _Iyear_3 |   .5547506   .0370356    14.98   0.000     .4821621    .6273391
           _cons |   5.291402   .7181648     7.37   0.000     3.883825     6.69898
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           15.024                 2.376
Constant effect |            2.958                 2.158
Dominance       |            0.000                 2.467
Exogeneity      |            3.704                 2.582
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           15.024                 2.028
Constant effect |            2.958                 1.884
Dominance       |            0.000                 2.059
Exogeneity      |            3.704                 2.352
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_hwy1947 ), robust                       
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   12430.52
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9443
                                                  Root MSE        =     .32325

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.081166   .0832774    12.98   0.000     .9179451    1.244386
           l_pop |   .2855688     .06766     4.22   0.000     .1529576      .41818
   S_somecollege |   1.048668   .2363577     4.44   0.000     .5854157    1.511921
   l_mean_income |   -.697196   .2194139    -3.18   0.001    -1.127239   -.2671525
          S_poor |  -.5043539   .3639244    -1.39   0.166    -1.217633    .2089248
         S_manuf |   .6832022   .2625661     2.60   0.009     .1685821    1.197822
            div2 |  -.1424039   .0764912    -1.86   0.063    -.2923239    .0075162
            div3 |   .0661042   .0955148     0.69   0.489    -.1211014    .2533099
            div4 |  -.0880574   .1027834    -0.86   0.392    -.2895091    .1133943
            div5 |  -.1154341     .09311    -1.24   0.215    -.2979263    .0670581
            div6 |  -.2268524   .1040005    -2.18   0.029    -.4306897   -.0230151
            div7 |  -.1161813   .1123186    -1.03   0.301    -.3363216     .103959
            div8 |  -.3773249   .1315204    -2.87   0.004    -.6351001   -.1195498
            div9 |  -.2754086   .1208684    -2.28   0.023    -.5123064   -.0385108
elevat_range_msa |  -.0496384    .033237    -1.49   0.135    -.1147817    .0155049
  ruggedness_msa |   7.096535   2.013841     3.52   0.000     3.149479    11.04359
      heating_dd |   -.014067   .0027911    -5.04   0.000    -.0195374   -.0085966
      cooling_dd |   -.018368   .0059643    -3.08   0.002    -.0300579   -.0066782
          sprawl |   .0011609   .0020537     0.57   0.572    -.0028644    .0051861
        _Iyear_2 |   .2852633   .0507936     5.62   0.000     .1857096     .384817
        _Iyear_3 |   .4129282   .0909311     4.54   0.000     .2347065    .5911498
           _cons |   11.66684   1.994238     5.85   0.000     7.758203    15.57547
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 xi: ivqregress iqr l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_hwy1947 ) ,  quantile(10 25 50 75 90) vc
> e(robust)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(105) = 36708.94
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.487655   .2107463     7.06   0.000       1.0746     1.90071
           l_pop |   .0542207   .1570139     0.35   0.730    -.2535209    .3619624
   S_somecollege |   .9919641   .5066379     1.96   0.050    -.0010279    1.984956
   l_mean_income |  -.9830338   .4442412    -2.21   0.027    -1.853731    -.112337
          S_poor |  -.6748437   .7387651    -0.91   0.361    -2.122797    .7731093
         S_manuf |   .7168937   .3570876     2.01   0.045     .0170148    1.416773
            div2 |   .2562049   .4707856     0.54   0.586     -.666518    1.178928
            div3 |   .4436173   .4958057     0.89   0.371     -.528144    1.415379
            div4 |   .2308119   .4719479     0.49   0.625    -.6941891    1.155813
            div5 |   .3187783   .5308143     0.60   0.548    -.7215986    1.359155
            div6 |   .2224574   .5106996     0.44   0.663    -.7784954     1.22341
            div7 |    .200593   .5461461     0.37   0.713    -.8698336     1.27102
            div8 |  -.3227537    .524928    -0.61   0.539    -1.351594    .7060862
            div9 |   .2640911   .6863679     0.38   0.700    -1.081165    1.609348
elevat_range_msa |  -.2126372    .077245    -2.75   0.006    -.3640347   -.0612397
  ruggedness_msa |   17.51524   4.830286     3.63   0.000     8.048058    26.98243
      heating_dd |  -.0142031   .0063269    -2.24   0.025    -.0266036   -.0018027
      cooling_dd |  -.0159481   .0108422    -1.47   0.141    -.0371984    .0053022
          sprawl |   .0031786   .0044023     0.72   0.470    -.0054497    .0118069
        _Iyear_2 |   .2874072   .0903738     3.18   0.001     .1102779    .4645366
        _Iyear_3 |   .4091865   .1648343     2.48   0.013     .0861172    .7322557
           _cons |   13.85237   3.861218     3.59   0.000      6.28452    21.42021
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.205069   .0850731    14.17   0.000     1.038329    1.371809
           l_pop |   .1684698    .071309     2.36   0.018     .0287067    .3082329
   S_somecollege |    .977912   .4888798     2.00   0.045     .0197251    1.936099
   l_mean_income |  -.8434225   .4142745    -2.04   0.042    -1.655386   -.0314594
          S_poor |  -.8891534    .606886    -1.47   0.143    -2.078628    .3003213
         S_manuf |   .7702395   .2555009     3.01   0.003     .2694669    1.271012
            div2 |  -.1391022   .1525637    -0.91   0.362    -.4381215    .1599171
            div3 |    .058666   .1582411     0.37   0.711    -.2514808    .3688128
            div4 |  -.1223781   .1499684    -0.82   0.414    -.4163107    .1715546
            div5 |  -.2133254   .1773085    -1.20   0.229    -.5608437     .134193
            div6 |  -.2609892   .1865114    -1.40   0.162    -.6265448    .1045664
            div7 |   -.151622   .2146361    -0.71   0.480    -.5723011    .2690571
            div8 |  -.7208272   .2213198    -3.26   0.001    -1.154606   -.2870484
            div9 |  -.3412152   .2510127    -1.36   0.174     -.833191    .1507606
elevat_range_msa |   -.062855   .0504509    -1.25   0.213    -.1617369    .0360269
  ruggedness_msa |   10.60768    3.29581     3.22   0.001     4.148008    17.06735
      heating_dd |  -.0183226   .0042948    -4.27   0.000    -.0267404   -.0099049
      cooling_dd |  -.0246514   .0082122    -3.00   0.003     -.040747   -.0085558
          sprawl |   .0004141    .003108     0.13   0.894    -.0056775    .0065056
        _Iyear_2 |   .3482687   .0780528     4.46   0.000      .195288    .5012494
        _Iyear_3 |   .4401403    .156502     2.81   0.005      .133402    .7468785
           _cons |   13.93753   3.808333     3.66   0.000     6.473335    21.40172
-----------------+----------------------------------------------------------------
q50              |
            l_ln |    .962907   .0918607    10.48   0.000     .7828633    1.142951
           l_pop |   .3363788   .0811375     4.15   0.000     .1773523    .4954053
   S_somecollege |   .7601815   .2833421     2.68   0.007     .2048412    1.315522
   l_mean_income |  -.6985945   .2547343    -2.74   0.006    -1.197865   -.1993244
          S_poor |  -.3849316   .4608418    -0.84   0.404    -1.288165    .5183018
         S_manuf |    .100316   .3580759     0.28   0.779       -.6015     .802132
            div2 |  -.2182631   .0961742    -2.27   0.023    -.4067611   -.0297652
            div3 |   .0734195   .1135523     0.65   0.518     -.149139    .2959779
            div4 |  -.1182968    .133054    -0.89   0.374    -.3790779    .1424842
            div5 |  -.2028188   .1238931    -1.64   0.102    -.4456449    .0400072
            div6 |  -.2231516   .1359248    -1.64   0.101    -.4895592     .043256
            div7 |  -.1629823   .1370579    -1.19   0.234    -.4316108    .1056463
            div8 |  -.4580775   .2214109    -2.07   0.039    -.8920349   -.0241201
            div9 |  -.5046099   .2067587    -2.44   0.015    -.9098494   -.0993704
elevat_range_msa |  -.0176071   .0636126    -0.28   0.782    -.1422855    .1070714
  ruggedness_msa |   7.990438    2.01422     3.97   0.000     4.042639    11.93824
      heating_dd |  -.0191678   .0036997    -5.18   0.000    -.0264191   -.0119165
      cooling_dd |  -.0322248   .0074628    -4.32   0.000    -.0468515    -.017598
          sprawl |  -.0009035   .0027773    -0.33   0.745    -.0063469    .0045399
        _Iyear_2 |   .2827351   .0617659     4.58   0.000     .1616763     .403794
        _Iyear_3 |   .4402846   .1088761     4.04   0.000     .2268914    .6536777
           _cons |   12.62716   2.224367     5.68   0.000     8.267478    16.98684
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8471141   .0903837     9.37   0.000     .6699653    1.024263
           l_pop |   .3787694   .0734548     5.16   0.000     .2348007    .5227381
   S_somecollege |    .274595   .2908955     0.94   0.345    -.2955496    .8447397
   l_mean_income |  -.1207578   .2503593    -0.48   0.630    -.6114531    .3699374
          S_poor |  -.3215228   .4972112    -0.65   0.518    -1.296039    .6529932
         S_manuf |   -.246368   .2353136    -1.05   0.295    -.7075743    .2148382
            div2 |  -.2938501   .1316023    -2.23   0.026    -.5517858   -.0359144
            div3 |   .0146211   .1458655     0.10   0.920    -.2712699    .3005122
            div4 |  -.1659286   .1545764    -1.07   0.283    -.4688926    .1370355
            div5 |  -.0918937   .1479549    -0.62   0.535    -.3818801    .1980927
            div6 |  -.1223514   .1580374    -0.77   0.439    -.4320989    .1873962
            div7 |  -.1007514   .1625414    -0.62   0.535    -.4193266    .2178238
            div8 |  -.2232004   .2091841    -1.07   0.286    -.6331938     .186793
            div9 |  -.2601196   .2171802    -1.20   0.231     -.685785    .1655457
elevat_range_msa |  -.0641606   .0640338    -1.00   0.316    -.1896645    .0613433
  ruggedness_msa |   6.497879   2.609218     2.49   0.013     1.383904    11.61185
      heating_dd |  -.0105562   .0032338    -3.26   0.001    -.0168943   -.0042181
      cooling_dd |   -.016406   .0064169    -2.56   0.011    -.0289828   -.0038291
          sprawl |   .0007392   .0024109     0.31   0.759    -.0039861    .0054645
        _Iyear_2 |   .2982945   .0645328     4.62   0.000     .1718126    .4247765
        _Iyear_3 |   .4552918   .1136291     4.01   0.000     .2325829    .6780006
           _cons |   6.594806   2.333637     2.83   0.005     2.020961    11.16865
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .9089617   .1795639     5.06   0.000     .5570228    1.260901
           l_pop |   .3256063    .136722     2.38   0.017     .0576361    .5935766
   S_somecollege |   1.107238   .3622661     3.06   0.002     .3972094    1.817266
   l_mean_income |  -.2829971   .3957441    -0.72   0.475    -1.058641    .4926472
          S_poor |  -.5664518   .5700491    -0.99   0.320    -1.683727    .5508238
         S_manuf |   .3312721    .321658     1.03   0.303     -.299166    .9617102
            div2 |  -.2761475   .1074063    -2.57   0.010      -.48666   -.0656351
            div3 |  -.0178901   .1188105    -0.15   0.880    -.2507543    .2149741
            div4 |  -.2518396   .1530735    -1.65   0.100    -.5518581    .0481789
            div5 |  -.2981534   .1396704    -2.13   0.033    -.5719023   -.0244044
            div6 |  -.3740223   .1622527    -2.31   0.021    -.6920318   -.0560129
            div7 |  -.3012483   .1338549    -2.25   0.024     -.563599   -.0388976
            div8 |  -.4010794   .1407988    -2.85   0.004    -.6770401   -.1251188
            div9 |  -.3933779   .1217315    -3.23   0.001    -.6319673   -.1547885
elevat_range_msa |  -.0058272   .0416362    -0.14   0.889    -.0874327    .0757783
  ruggedness_msa |   6.209996   1.849818     3.36   0.001      2.58442    9.835572
      heating_dd |  -.0057416   .0035229    -1.63   0.103    -.0126464    .0011631
      cooling_dd |   .0090034   .0094838     0.95   0.342    -.0095846    .0275913
          sprawl |   .0027941   .0022732     1.23   0.219    -.0016613    .0072494
        _Iyear_2 |   .1742591   .0846811     2.06   0.040     .0082872     .340231
        _Iyear_3 |   .2880086   .1586159     1.82   0.069    -.0228729    .5988901
           _cons |   7.936294   4.213111     1.88   0.060    -.3212515    16.19384
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           13.642                 2.384
Constant effect |            2.744                 2.309
Dominance       |            0.000                 2.149
Exogeneity      |            3.704                 2.381
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           13.642                 2.176
Constant effect |            2.744                 1.694
Dominance       |            0.000                 2.012
Exogeneity      |            3.704                 2.065
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_hwy1947 ), robust       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   13091.02
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9442
                                                  Root MSE        =     .32356

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.120968   .0926627    12.10   0.000     .9393528    1.302584
           l_pop |   .1944544   .1402892     1.39   0.166    -.0805074    .4694163
         l_pop80 |   .6508502   .3008699     2.16   0.031     .0611561    1.240544
         l_pop70 |  -.0990195   .3787314    -0.26   0.794    -.8413194    .6432805
         l_pop60 |  -.6090298    .298713    -2.04   0.041    -1.194497    -.023563
         l_pop50 |   .0281836   .1856892     0.15   0.879    -.3357606    .3921277
         l_pop40 |  -.0421996   .2008207    -0.21   0.834    -.4358008    .3514017
         l_pop30 |   .1666581     .17037     0.98   0.328     -.167261    .5005771
         l_pop20 |  -.0311454   .0846005    -0.37   0.713    -.1969593    .1346685
   S_somecollege |   .4874998   .3083282     1.58   0.114    -.1168124    1.091812
   l_mean_income |  -.5751417   .2226727    -2.58   0.010    -1.011572   -.1387112
          S_poor |  -.2865847    .369927    -0.77   0.439    -1.011628    .4384589
         S_manuf |   .6388746    .268167     2.38   0.017      .113277    1.164472
            div2 |  -.1627557   .0822418    -1.98   0.048    -.3239466   -.0015649
            div3 |   .0299122     .10374     0.29   0.773    -.1734146    .2332389
            div4 |  -.0837289   .1050508    -0.80   0.425    -.2896247     .122167
            div5 |  -.1593872    .101487    -1.57   0.116    -.3582981    .0395238
            div6 |  -.2533182   .1107671    -2.29   0.022    -.4704176   -.0362187
            div7 |  -.1039064   .1185231    -0.88   0.381    -.3362075    .1283947
            div8 |  -.4588864   .1557926    -2.95   0.003    -.7642342   -.1535385
            div9 |  -.3144056    .147565    -2.13   0.033    -.6036276   -.0251836
elevat_range_msa |  -.0621414   .0330232    -1.88   0.060    -.1268656    .0025828
  ruggedness_msa |   5.438858   2.042281     2.66   0.008     1.436061    9.441656
      heating_dd |  -.0161294   .0029858    -5.40   0.000    -.0219815   -.0102773
      cooling_dd |  -.0299701   .0067518    -4.44   0.000    -.0432033   -.0167369
          sprawl |   .0002619   .0021762     0.12   0.904    -.0040034    .0045272
        _Iyear_2 |   .3610268   .0574462     6.28   0.000     .2484343    .4736193
        _Iyear_3 |   .5685283   .1080237     5.26   0.000     .3568058    .7802508
           _cons |   10.90906   2.033992     5.36   0.000     6.922507    14.89561
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 xi: ivqregress iqr l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_hwy1947 ) ,  quantile(10 25 50 75 90)  vce(robust)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(140) = 30547.81
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.459338   .1627867     8.96   0.000     1.140281    1.778394
           l_pop |   .3215899   .1476315     2.18   0.029     .0322375    .6109423
         l_pop80 |  -.1589788   .4078937    -0.39   0.697    -.9584358    .6404782
         l_pop70 |    .595199   .6017729     0.99   0.323    -.5842542    1.774652
         l_pop60 |   -1.03608    .665913    -1.56   0.120    -2.341246    .2690851
         l_pop50 |   .2277236   .4681647     0.49   0.627    -.6898623     1.14531
         l_pop40 |   .3806879   .2725069     1.40   0.162    -.1534159    .9147917
         l_pop30 |  -.1833719   .3139887    -0.58   0.559    -.7987785    .4320347
         l_pop20 |  -.1163926   .1764282    -0.66   0.509    -.4621854    .2294003
   S_somecollege |   .6855583   .5654066     1.21   0.225    -.4226184    1.793735
   l_mean_income |  -.7590646    .456668    -1.66   0.096    -1.654117    .1359882
          S_poor |  -.4799456    .544637    -0.88   0.378    -1.547415    .5875234
         S_manuf |   .8727001   .3164148     2.76   0.006     .2525386    1.492862
            div2 |   .1580524   .5168964     0.31   0.760     -.855046    1.171151
            div3 |   .2803415   .5407582     0.52   0.604    -.7795251    1.340208
            div4 |    .188704   .5133479     0.37   0.713    -.8174395    1.194847
            div5 |   .1639054   .5709633     0.29   0.774     -.955162    1.282973
            div6 |   .1642248   .5586811     0.29   0.769    -.9307701     1.25922
            div7 |   .0258672   .5911846     0.04   0.965    -1.132833    1.184568
            div8 |  -.4629731   .5548667    -0.83   0.404    -1.550492    .6245457
            div9 |   .1587785   .6758822     0.23   0.814    -1.165926    1.483483
elevat_range_msa |  -.1944807   .0826686    -2.35   0.019    -.3565082   -.0324532
  ruggedness_msa |   12.58559    4.11412     3.06   0.002     4.522065    20.64912
      heating_dd |  -.0156488    .004905    -3.19   0.001    -.0252624   -.0060352
      cooling_dd |  -.0250113   .0106254    -2.35   0.019    -.0458367   -.0041859
          sprawl |   .0009007   .0038362     0.23   0.814    -.0066181    .0084194
        _Iyear_2 |   .2854967   .0970559     2.94   0.003     .0952707    .4757228
        _Iyear_3 |   .4505167   .1731056     2.60   0.009     .1112359    .7897975
           _cons |   12.55835   3.941362     3.19   0.001     4.833426    20.28328
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.195613   .0995457    12.01   0.000     1.000507    1.390719
           l_pop |     .20951   .1405695     1.49   0.136    -.0660012    .4850213
         l_pop80 |   .5893884   .3030282     1.94   0.052     -.004536    1.183313
         l_pop70 |   .0119094   .3563483     0.03   0.973    -.6865205    .7103393
         l_pop60 |  -.9273059   .4369091    -2.12   0.034    -1.783632   -.0709798
         l_pop50 |   .1215314    .326811     0.37   0.710    -.5190065    .7620692
         l_pop40 |   .1909562   .2416578     0.79   0.429    -.2826845    .6645968
         l_pop30 |  -.0477348   .1717759    -0.28   0.781    -.3844093    .2889397
         l_pop20 |   .0337786   .0895698     0.38   0.706     -.141775    .2093322
   S_somecollege |   .2992541   .4257069     0.70   0.482     -.535116    1.133624
   l_mean_income |   -.400882   .3203204    -1.25   0.211    -1.028699    .2269345
          S_poor |  -.6193282   .4848124    -1.28   0.201    -1.569543    .3308866
         S_manuf |   .5650249   .2602921     2.17   0.030     .0548617    1.075188
            div2 |  -.0665265   .1398664    -0.48   0.634    -.3406596    .2076065
            div3 |   .1078872   .1518013     0.71   0.477    -.1896378    .4054122
            div4 |  -.0152827   .1455253    -0.11   0.916     -.300507    .2699415
            div5 |  -.1524393   .1624663    -0.94   0.348    -.4708674    .1659888
            div6 |  -.2211509   .1663068    -1.33   0.184    -.5471062    .1048043
            div7 |  -.0858219    .195422    -0.44   0.661     -.468842    .2971982
            div8 |  -.7030789    .205721    -3.42   0.001    -1.106285   -.2998731
            div9 |  -.3281635   .2570256    -1.28   0.202    -.8319245    .1755975
elevat_range_msa |  -.0597755   .0452209    -1.32   0.186    -.1484068    .0288557
  ruggedness_msa |   9.026741   3.060131     2.95   0.003     3.028994    15.02449
      heating_dd |  -.0210082   .0041929    -5.01   0.000    -.0292262   -.0127902
      cooling_dd |  -.0346832   .0088445    -3.92   0.000    -.0520181   -.0173483
          sprawl |   .0011649   .0029168     0.40   0.690    -.0045519    .0068816
        _Iyear_2 |   .3851798   .0697801     5.52   0.000     .2484133    .5219464
        _Iyear_3 |   .5391137   .1282854     4.20   0.000     .2876789    .7905484
           _cons |     9.7917   2.776195     3.53   0.000     4.350458    15.23294
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   1.029681   .0904217    11.39   0.000     .8524575    1.206904
           l_pop |   .2312189   .1991342     1.16   0.246    -.1590769    .6215148
         l_pop80 |   .4907266     .50897     0.96   0.335    -.5068362    1.488289
         l_pop70 |   .1623497   .5649484     0.29   0.774    -.9449288    1.269628
         l_pop60 |  -.7793472    .404885    -1.92   0.054    -1.572907    .0142127
         l_pop50 |   .1522838   .2584227     0.59   0.556    -.3542153     .658783
         l_pop40 |  -.0345985   .2995759    -0.12   0.908    -.6217564    .5525594
         l_pop30 |   .0259689   .3000468     0.09   0.931     -.562112    .6140498
         l_pop20 |   .0271695   .1971563     0.14   0.890    -.3592497    .4135887
   S_somecollege |   .0598992   .4634265     0.13   0.897       -.8484    .9681984
   l_mean_income |  -.5459502   .2655409    -2.06   0.040    -1.066401   -.0254995
          S_poor |  -.2179128    .517781    -0.42   0.674    -1.232745    .7969192
         S_manuf |   .1028984   .2617324     0.39   0.694    -.4100877    .6158844
            div2 |  -.2601455    .104493    -2.49   0.013     -.464948    -.055343
            div3 |   .0149622   .1232282     0.12   0.903    -.2265605     .256485
            div4 |  -.1602925   .1290145    -1.24   0.214    -.4131563    .0925714
            div5 |  -.2622764   .1298717    -2.02   0.043    -.5168203   -.0077325
            div6 |  -.2457028   .1413173    -1.74   0.082    -.5226796     .031274
            div7 |  -.2169884   .1401439    -1.55   0.122    -.4916653    .0576885
            div8 |  -.6074563   .2244967    -2.71   0.007    -1.047462   -.1674509
            div9 |   -.591059   .2084755    -2.84   0.005    -.9996636   -.1824545
elevat_range_msa |  -.0067889   .0463006    -0.15   0.883    -.0975363    .0839585
  ruggedness_msa |   5.859111    2.77482     2.11   0.035     .4205637    11.29766
      heating_dd |  -.0210299   .0035009    -6.01   0.000    -.0278915   -.0141684
      cooling_dd |  -.0418923   .0087488    -4.79   0.000    -.0590396    -.024745
          sprawl |  -.0030808     .00276    -1.12   0.264    -.0084904    .0023288
        _Iyear_2 |   .3717329    .083953     4.43   0.000     .2071881    .5362776
        _Iyear_3 |   .6038967   .1491125     4.05   0.000     .3116416    .8961517
           _cons |   11.80267    2.40923     4.90   0.000     7.080663    16.52467
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8927349   .1311858     6.81   0.000     .6356155    1.149854
           l_pop |   .1824771   .2099865     0.87   0.385    -.2290889     .594043
         l_pop80 |   .7005649   .6005267     1.17   0.243    -.4764459    1.877576
         l_pop70 |   -.256505    .702438    -0.37   0.715    -1.633258    1.120248
         l_pop60 |  -.3111002   .4366225    -0.71   0.476    -1.166865     .544664
         l_pop50 |  -.0457638   .2691303    -0.17   0.865    -.5732496    .4817219
         l_pop40 |  -.1157603   .3484486    -0.33   0.740     -.798707    .5671863
         l_pop30 |   .2297764   .2290946     1.00   0.316    -.2192408    .6787936
         l_pop20 |  -.0112483   .1381089    -0.08   0.935    -.2819367    .2594402
   S_somecollege |    .081348   .4712382     0.17   0.863    -.8422619    1.004958
   l_mean_income |  -.1358455   .2972423    -0.46   0.648    -.7184297    .4467387
          S_poor |  -.0095755   .5260931    -0.02   0.985    -1.040699    1.021548
         S_manuf |  -.2732102   .3318046    -0.82   0.410    -.9235353    .3771148
            div2 |   -.311885   .0804517    -3.88   0.000    -.4695675   -.1542025
            div3 |   .0009968    .080153     0.01   0.990    -.1561002    .1580939
            div4 |  -.1786522   .0943047    -1.89   0.058     -.363486    .0061817
            div5 |  -.0785051   .0985763    -0.80   0.426    -.2717111    .1147008
            div6 |  -.1611319   .1004648    -1.60   0.109    -.3580394    .0357755
            div7 |  -.1324196   .1283156    -1.03   0.302    -.3839135    .1190743
            div8 |  -.1933642   .1863044    -1.04   0.299     -.558514    .1717857
            div9 |  -.1633018   .1367665    -1.19   0.232    -.4313591    .1047555
elevat_range_msa |  -.0900058   .0498826    -1.80   0.071     -.187774    .0077623
  ruggedness_msa |   5.339157   2.457614     2.17   0.030     .5223217    10.15599
      heating_dd |  -.0082677    .004141    -2.00   0.046    -.0163839   -.0001514
      cooling_dd |  -.0148939   .0115948    -1.28   0.199    -.0376192    .0078313
          sprawl |   .0013485   .0028182     0.48   0.632     -.004175     .006872
        _Iyear_2 |   .3549431   .0998525     3.55   0.000     .1592358    .5506504
        _Iyear_3 |   .5898944   .1825541     3.23   0.001     .2320949    .9476939
           _cons |   6.533791   2.825586     2.31   0.021     .9957436    12.07184
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8919803   .1685683     5.29   0.000     .5615925    1.222368
           l_pop |    .219613    .269626     0.81   0.415    -.3088442    .7480703
         l_pop80 |   .3331755   .8070315     0.41   0.680    -1.248577    1.914928
         l_pop70 |   .4263441    1.15462     0.37   0.712    -1.836669    2.689357
         l_pop60 |  -.8370205   .8312083    -1.01   0.314    -2.466159    .7921179
         l_pop50 |   .4465981   .9578719     0.47   0.641    -1.430796    2.323993
         l_pop40 |  -.5109578   .5696265    -0.90   0.370    -1.627405    .6054896
         l_pop30 |   .4628462   .3536169     1.31   0.191    -.2302301    1.155923
         l_pop20 |  -.1848088   .2463773    -0.75   0.453    -.6676995    .2980819
   S_somecollege |   .2751995   .7176547     0.38   0.701    -1.131378    1.681777
   l_mean_income |  -.3510807   .5511265    -0.64   0.524    -1.431269    .7291075
          S_poor |  -.5698279   .7458944    -0.76   0.445    -2.031754    .8920982
         S_manuf |   .2434867   .4718427     0.52   0.606     -.681308    1.168281
            div2 |  -.3036788    .124969    -2.43   0.015    -.5486137    -.058744
            div3 |  -.0262184    .143643    -0.18   0.855    -.3077534    .2553167
            div4 |  -.1860634   .1994839    -0.93   0.351    -.5770447    .2049178
            div5 |  -.2723446   .1488242    -1.83   0.067    -.5640346    .0193455
            div6 |  -.3208877   .1580636    -2.03   0.042    -.6306866   -.0110888
            div7 |  -.2188779   .1476453    -1.48   0.138    -.5082572    .0705015
            div8 |  -.2776702   .2896402    -0.96   0.338    -.8453546    .2900142
            div9 |  -.2346935   .2423572    -0.97   0.333    -.7097048    .2403178
elevat_range_msa |  -.0391088   .0606893    -0.64   0.519    -.1580575      .07984
  ruggedness_msa |   5.034159   3.311246     1.52   0.128    -1.455764    11.52408
      heating_dd |  -.0034497   .0067438    -0.51   0.609    -.0166672    .0097679
      cooling_dd |   .0057639   .0170602     0.34   0.735    -.0276734    .0392012
          sprawl |   .0046357   .0032958     1.41   0.160     -.001824    .0110953
        _Iyear_2 |   .2744154   .1215423     2.26   0.024     .0361969    .5126338
        _Iyear_3 |   .4554946   .2368568     1.92   0.054    -.0087362    .9197253
           _cons |   8.346392   5.399802     1.55   0.122    -2.237025    18.92981
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           11.452                 2.220
Constant effect |            2.828                 2.321
Dominance       |            0.000                 2.222
Exogeneity      |            2.936                 2.243
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           11.452                 2.048
Constant effect |            2.828                 2.110
Dominance       |            0.000                 1.951
Exogeneity      |            2.936                 2.144
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
.         
.         
. * Third Panel * 
.                 
.                 local Inst "l_rail1898"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_rail1898 ), robust   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    1536.48
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8744
                                                  Root MSE        =      .4853

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.313813   .0393074    33.42   0.000     1.236772    1.390854
    _Iyear_2 |   .3990986   .0470344     8.49   0.000     .3069128    .4912843
    _Iyear_3 |   .6653059   .0465901    14.28   0.000      .573991    .7566208
       _cons |   6.293648   .2603771    24.17   0.000     5.783318    6.803978
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898

. 
.                 xi: ivqregress iqr l_vmt i.year (l_ln =  l_rail1898 ) ,  quantile(1
> 0 25 50 75 90) bound(1 2) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     684
Estimator: Inverse quantile regression                 Wald chi2(15) = 5306.03
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.492588   .0394781    37.81   0.000     1.415213    1.569964
    _Iyear_2 |   .3755544   .1359988     2.76   0.006     .1090016    .6421072
    _Iyear_3 |   .6657766   .1272984     5.23   0.000     .4162763     .915277
       _cons |   4.453017   .3016457    14.76   0.000     3.861803    5.044232
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.393183    .027808    50.10   0.000      1.33868    1.447685
    _Iyear_2 |   .4629735   .0549569     8.42   0.000       .35526    .5706871
    _Iyear_3 |   .7122253   .0569813    12.50   0.000      .600544    .8239065
       _cons |   5.492935   .1954375    28.11   0.000     5.109885    5.875986
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.272771   .0247332    51.46   0.000     1.224294    1.321247
    _Iyear_2 |   .4608159   .0440725    10.46   0.000     .3744354    .5471965
    _Iyear_3 |   .6833102   .0468268    14.59   0.000     .5915313    .7750891
       _cons |   6.612667   .1731158    38.20   0.000     6.273367    6.951968
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.191677   .0228595    52.13   0.000     1.146873     1.23648
    _Iyear_2 |    .353255   .0450721     7.84   0.000     .2649153    .4415948
    _Iyear_3 |   .6155769   .0441434    13.94   0.000     .5290574    .7020965
       _cons |     7.4157    .169968    43.63   0.000     7.082568    7.748831
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.134839    .024886    45.60   0.000     1.086064    1.183615
    _Iyear_2 |    .341813   .0531895     6.43   0.000     .2375634    .4460626
    _Iyear_3 |   .5508853   .0496581    11.09   0.000     .4535573    .6482133
       _cons |   8.037776   .1925327    41.75   0.000     7.660419    8.415134
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           51.460                 2.524
Constant effect |            8.784                 2.250
Dominance       |            0.000                 2.288
Exogeneity      |            2.106                 2.433
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           51.460                 2.227
Constant effect |            8.784                 2.042
Dominance       |            0.000                 2.167
Exogeneity      |            2.106                 2.072
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln =  l_rail1898 ), robus
> t    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7689.13
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9360
                                                  Root MSE        =     .34629

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .8308198   .0956895     8.68   0.000     .6432719    1.018368
       l_pop |   .4691948   .0669826     7.00   0.000     .3379114    .6004783
    _Iyear_2 |   .3964238   .0337989    11.73   0.000     .3301791    .4626684
    _Iyear_3 |   .6273888   .0333302    18.82   0.000     .5620629    .6927148
       _cons |   3.460594   .2702965    12.80   0.000     2.930822    3.990365
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898

. 
.                 xi: ivqregress iqr l_vmt l_pop i.year (l_ln =  l_rail1898 ) ,  quan
> tile(10 25 50 75 90) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(20) = 13862.26
                                                      Prob > chi2   =   0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   .9923448   .0747819    13.27   0.000      .845775    1.138915
       l_pop |   .4127345    .048938     8.43   0.000     .3168179    .5086512
    _Iyear_2 |   .3795715   .0995753     3.81   0.000     .1844074    .5747356
    _Iyear_3 |   .6111602   .0832762     7.34   0.000     .4479419    .7743785
       _cons |   2.603028   .2314678    11.25   0.000     2.149359    3.056696
-------------+----------------------------------------------------------------
q25          |
        l_ln |   .8985088   .0790001    11.37   0.000     .7436715    1.053346
       l_pop |   .4392291   .0568815     7.72   0.000     .3277435    .5507147
    _Iyear_2 |   .4495505   .0390668    11.51   0.000     .3729809      .52612
    _Iyear_3 |   .6575193   .0414114    15.88   0.000     .5763544    .7386842
       _cons |   3.252272   .2536114    12.82   0.000     2.755203    3.749341
-------------+----------------------------------------------------------------
q50          |
        l_ln |   .7636417   .1012234     7.54   0.000     .5652476    .9620359
       l_pop |   .5009997   .0773139     6.48   0.000     .3494673    .6525321
    _Iyear_2 |   .3843162   .0421469     9.12   0.000     .3017098    .4669225
    _Iyear_3 |   .6244432   .0438113    14.25   0.000     .5385746    .7103117
       _cons |   3.515421   .3562895     9.87   0.000     2.817107    4.213736
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .5753652   .0847309     6.79   0.000     .4092957    .7414347
       l_pop |   .6271775   .0664553     9.44   0.000     .4969275    .7574275
    _Iyear_2 |   .3352947   .0453564     7.39   0.000     .2463979    .4241916
    _Iyear_3 |   .5707586   .0446024    12.80   0.000     .4833395    .6581777
       _cons |   3.431646   .3536732     9.70   0.000      2.73846    4.124833
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .5969225   .0619056     9.64   0.000     .4755898    .7182552
       l_pop |   .5615887   .0463154    12.13   0.000     .4708122    .6523652
    _Iyear_2 |   .3072851   .0339343     9.06   0.000     .2407751    .3737951
    _Iyear_3 |   .5222304     .03418    15.28   0.000     .4552389    .5892218
       _cons |   4.202905   .2310155    18.19   0.000     3.750123    4.655687
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           12.237                 2.664
Constant effect |            4.337                 2.196
Dominance       |            0.000                 2.372
Exogeneity      |            3.212                 2.647
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           12.237                 2.386
Constant effect |            4.337                 2.057
Dominance       |            0.000                 2.261
Exogeneity      |            3.212                 2.226
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _rail1898 ), robust                               
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   12417.43
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9453
                                                  Root MSE        =     .32017

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.025713   .1136805     9.02   0.000     .8029031    1.248522
           l_pop |   .3047686   .0904469     3.37   0.001      .127496    .4820412
            div2 |  -.1740415   .0639154    -2.72   0.006    -.2993133   -.0487696
            div3 |   .0453234   .0798257     0.57   0.570    -.1111321     .201779
            div4 |  -.0443604   .1027226    -0.43   0.666     -.245693    .1569721
            div5 |  -.0685146   .0987507    -0.69   0.488    -.2620624    .1250332
            div6 |  -.1725231   .1089175    -1.58   0.113    -.3859974    .0409512
            div7 |  -.1331172   .1172593    -1.14   0.256    -.3629413    .0967069
            div8 |  -.3783429    .157195    -2.41   0.016    -.6864396   -.0702463
            div9 |  -.3288215   .1319736    -2.49   0.013    -.5874849    -.070158
elevat_range_msa |   -.026167   .0348913    -0.75   0.453    -.0945528    .0422187
  ruggedness_msa |   6.668191   2.063906     3.23   0.001     2.623009    10.71337
      heating_dd |  -.0147949    .002821    -5.24   0.000    -.0203239   -.0092659
      cooling_dd |  -.0223321   .0055851    -4.00   0.000    -.0332788   -.0113854
          sprawl |   .0013906   .0021029     0.66   0.508    -.0027311    .0055122
        _Iyear_2 |    .394246   .0307025    12.84   0.000     .3340701    .4544218
        _Iyear_3 |   .6363618   .0307579    20.69   0.000     .5760773    .6966462
           _cons |   5.296225   .6820359     7.77   0.000     3.959459    6.632991
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898

.                 
.                 xi: ivqregress iqr l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year (l_ln =  l_
> rail1898 ) ,  quantile(10 25 50 75 90) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(85) = 24927.23
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.385218   .1298808    10.67   0.000     1.130656     1.63978
           l_pop |     .09976   .0983983     1.01   0.311    -.0930971    .2926171
            div2 |   .2024203   .4202422     0.48   0.630    -.6212392     1.02608
            div3 |   .2838391   .4202105     0.68   0.499    -.5397583    1.107436
            div4 |   .1561401   .4074016     0.38   0.702    -.6423524    .9546326
            div5 |   .1419219   .4402989     0.32   0.747    -.7210481    1.004892
            div6 |   .0703138   .4374781     0.16   0.872    -.7871275     .927755
            div7 |   .0792858   .4489578     0.18   0.860    -.8006553    .9592268
            div8 |  -.4123122   .4309338    -0.96   0.339    -1.256927    .4323025
            div9 |   .0874988   .5006878     0.17   0.861    -.8938312    1.068829
elevat_range_msa |  -.1682113   .0597101    -2.82   0.005    -.2852409   -.0511818
  ruggedness_msa |   15.62915   2.547883     6.13   0.000      10.6354    20.62291
      heating_dd |  -.0177725   .0043333    -4.10   0.000    -.0262656   -.0092793
      cooling_dd |  -.0232457   .0082936    -2.80   0.005    -.0395008   -.0069905
          sprawl |   .0045673    .002984     1.53   0.126    -.0012813     .010416
        _Iyear_2 |   .4271917   .0381707    11.19   0.000     .3523786    .5020049
        _Iyear_3 |   .6643036   .0396421    16.76   0.000     .5866065    .7420006
           _cons |   4.951584   .9019999     5.49   0.000     3.183696    6.719471
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.305841   .1599046     8.17   0.000      .992434    1.619248
           l_pop |   .0614521   .1297883     0.47   0.636    -.1929283    .3158326
            div2 |  -.2174023   .1624192    -1.34   0.181    -.5357381    .1009335
            div3 |  -.0310722   .1638463    -0.19   0.850    -.3522051    .2900608
            div4 |  -.1787187   .1779758    -1.00   0.315    -.5275448    .1701075
            div5 |  -.2611423   .1854137    -1.41   0.159    -.6245465    .1022619
            div6 |   -.357734    .187182    -1.91   0.056     -.724604    .0091359
            div7 |  -.2683736   .1996844    -1.34   0.179    -.6597479    .1230008
            div8 |  -.9622338   .2818675    -3.41   0.001    -1.514684   -.4097836
            div9 |   -.545507    .237878    -2.29   0.022    -1.011739   -.0792746
elevat_range_msa |  -.0284657   .0422789    -0.67   0.501    -.1113308    .0543995
  ruggedness_msa |   11.12355    2.86577     3.88   0.000     5.506741    16.74035
      heating_dd |  -.0219865   .0045024    -4.88   0.000    -.0308111    -.013162
      cooling_dd |  -.0336239   .0097637    -3.44   0.001    -.0527604   -.0144874
          sprawl |  -.0000799    .003129    -0.03   0.980    -.0062126    .0060528
        _Iyear_2 |   .4241431   .0478592     8.86   0.000     .3303407    .5179455
        _Iyear_3 |   .6566376   .0475636    13.81   0.000     .5634146    .7498607
           _cons |   6.946575   1.037378     6.70   0.000     4.913351    8.979799
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .8495174   .1560749     5.44   0.000     .5436162    1.155419
           l_pop |   .3883036   .1357864     2.86   0.004     .1221672      .65444
            div2 |  -.1129146   .1007036    -1.12   0.262      -.31029    .0844608
            div3 |   .1337772   .1212984     1.10   0.270    -.1039634    .3715178
            div4 |   .0118497   .1454756     0.08   0.935    -.2732773    .2969766
            div5 |  -.0335775   .1526124    -0.22   0.826    -.3326923    .2655373
            div6 |  -.0529728   .1788668    -0.30   0.767    -.4035452    .2975996
            div7 |  -.0625737   .1676034    -0.37   0.709    -.3910704    .2659229
            div8 |  -.2871696   .2531778    -1.13   0.257     -.783389    .2090498
            div9 |  -.3045514   .2543315    -1.20   0.231    -.8030319    .1939291
elevat_range_msa |  -.0098803   .0619693    -0.16   0.873    -.1313379    .1115774
  ruggedness_msa |   6.562829   2.820281     2.33   0.020     1.035179    12.09048
      heating_dd |  -.0161627   .0050585    -3.20   0.001    -.0260772   -.0062482
      cooling_dd |  -.0247034   .0095937    -2.57   0.010    -.0435067   -.0059001
          sprawl |  -.0006392   .0028028    -0.23   0.820    -.0061325    .0048542
        _Iyear_2 |   .3975459   .0414679     9.59   0.000     .3162703    .4788215
        _Iyear_3 |   .6495386    .042642    15.23   0.000     .5659619    .7331154
           _cons |   5.450909   1.180214     4.62   0.000     3.137733    7.764086
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8728091   .1010898     8.63   0.000     .6746768    1.070941
           l_pop |   .3927418   .0818206     4.80   0.000     .2323762    .5531073
            div2 |  -.3255471   .0676761    -4.81   0.000    -.4581899   -.1929044
            div3 |   -.008991   .0642763    -0.14   0.889    -.1349703    .1169882
            div4 |  -.1561659   .0830395    -1.88   0.060    -.3189204    .0065886
            div5 |  -.1064921   .0951776    -1.12   0.263    -.2930367    .0800525
            div6 |  -.1590184   .0872261    -1.82   0.068    -.3299785    .0119417
            div7 |  -.1214877   .1035243    -1.17   0.241    -.3243915    .0814161
            div8 |  -.2166425   .1184227    -1.83   0.067    -.4487467    .0154618
            div9 |  -.2529721   .1360949    -1.86   0.063    -.5197131    .0137689
elevat_range_msa |  -.0561227   .0511395    -1.10   0.272    -.1563543    .0441088
  ruggedness_msa |   7.869188   2.081542     3.78   0.000     3.789441    11.94894
      heating_dd |  -.0076237    .003259    -2.34   0.019    -.0140113   -.0012361
      cooling_dd |  -.0075259   .0082416    -0.91   0.361    -.0236792    .0086274
          sprawl |    .000687   .0019535     0.35   0.725    -.0031418    .0045158
        _Iyear_2 |   .3616714   .0357264    10.12   0.000     .2916488    .4316939
        _Iyear_3 |   .5711434   .0386566    14.77   0.000     .4953779    .6469088
           _cons |   4.943537   .5283366     9.36   0.000     3.908016    5.979058
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8103314   .1460503     5.55   0.000     .5240781    1.096585
           l_pop |   .4150351   .1240893     3.34   0.001     .1718245    .6582456
            div2 |  -.3497631   .0847955    -4.12   0.000    -.5159593   -.1835669
            div3 |  -.0432378   .0867625    -0.50   0.618    -.2132892    .1268137
            div4 |   -.118811   .1252343    -0.95   0.343    -.3642656    .1266437
            div5 |  -.1801318   .1363172    -1.32   0.186    -.4473085     .087045
            div6 |  -.2715254   .1394312    -1.95   0.051    -.5448054    .0017547
            div7 |  -.2555678   .1648261    -1.55   0.121     -.578621    .0674854
            div8 |  -.2254067   .1659477    -1.36   0.174    -.5506583    .0998448
            div9 |  -.2835724   .1100107    -2.58   0.010    -.4991894   -.0679553
elevat_range_msa |   -.005043   .0547446    -0.09   0.927    -.1123405    .1022544
  ruggedness_msa |    5.38486   2.345779     2.30   0.022     .7872171    9.982503
      heating_dd |  -.0041902   .0030828    -1.36   0.174    -.0102324    .0018519
      cooling_dd |   .0065724   .0096589     0.68   0.496    -.0123588    .0255036
          sprawl |   .0026412   .0030218     0.87   0.382    -.0032814    .0085638
        _Iyear_2 |   .3287712    .038614     8.51   0.000     .2530891    .4044533
        _Iyear_3 |   .5470187   .0434673    12.58   0.000     .4618244     .632213
           _cons |   4.851233   .7826031     6.20   0.000     3.317359    6.385107
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           10.362                 2.454
Constant effect |            3.451                 2.175
Dominance       |            0.000                 2.521
Exogeneity      |            2.370                 2.516
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           10.362                 2.344
Constant effect |            3.451                 1.917
Dominance       |            0.000                 2.172
Exogeneity      |            2.370                 2.151
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_rail1898 ), robust                      
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   13484.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9492
                                                  Root MSE        =     .30858

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9996302    .110914     9.01   0.000     .7822426    1.217018
           l_pop |   .3508872   .0877359     4.00   0.000      .178928    .5228464
   S_somecollege |   1.049819   .2226508     4.72   0.000     .6134312    1.486206
   l_mean_income |  -.7404591   .2130044    -3.48   0.001     -1.15794   -.3229781
          S_poor |   -.518726   .3518197    -1.47   0.140     -1.20828    .1708279
         S_manuf |    .504818   .3193398     1.58   0.114    -.1210766    1.130713
            div2 |  -.1212761   .0734843    -1.65   0.099    -.2653026    .0227504
            div3 |   .1125746   .0945313     1.19   0.234    -.0727034    .2978525
            div4 |  -.0352704   .1032982    -0.34   0.733    -.2377311    .1671903
            div5 |  -.0809932   .0939684    -0.86   0.389    -.2651679    .1031814
            div6 |  -.1730638   .1120387    -1.54   0.122    -.3926556     .046528
            div7 |  -.0678017   .1187337    -0.57   0.568    -.3005155    .1649122
            div8 |  -.2974042   .1364372    -2.18   0.029    -.5648163   -.0299921
            div9 |  -.2331249     .11748    -1.98   0.047    -.4633815   -.0028684
elevat_range_msa |   -.051476   .0314581    -1.64   0.102    -.1131328    .0101808
  ruggedness_msa |   6.751279   1.936501     3.49   0.000     2.955807    10.54675
      heating_dd |  -.0136213   .0025978    -5.24   0.000    -.0187129   -.0085296
      cooling_dd |  -.0185056   .0056134    -3.30   0.001    -.0295076   -.0075036
          sprawl |   .0023014   .0020992     1.10   0.273     -.001813    .0064158
        _Iyear_2 |   .2745891   .0501138     5.48   0.000     .1763678    .3728104
        _Iyear_3 |   .3892854   .0914941     4.25   0.000     .2099603    .5686105
           _cons |   11.73015   1.927587     6.09   0.000     7.952147    15.50815
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 xi: ivqregress iqr l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_rail1898 ) ,  quantile(10 25 50 75 90) v
> ce(robust)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(105) = 40805.70
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.381158   .1444243     9.56   0.000     1.098092    1.664225
           l_pop |   .1357389   .1129808     1.20   0.230    -.0856994    .3571772
   S_somecollege |   1.269556   .4407022     2.88   0.004     .4057953    2.133316
   l_mean_income |  -1.130279   .3658352    -3.09   0.002    -1.847303   -.4132554
          S_poor |  -1.001199   .5428043    -1.84   0.065    -2.065076     .062678
         S_manuf |   .9218595   .2911295     3.17   0.002     .3512562    1.492463
            div2 |   .2923135   .4144138     0.71   0.481    -.5199226     1.10455
            div3 |   .4714225   .4360751     1.08   0.280    -.3832691    1.326114
            div4 |   .1962906    .416729     0.47   0.638    -.6204831    1.013064
            div5 |   .3119454   .4691281     0.66   0.506    -.6075289     1.23142
            div6 |   .2625442   .4521869     0.58   0.562    -.6237259    1.148814
            div7 |   .2396736   .4824053     0.50   0.619    -.7058234    1.185171
            div8 |  -.1499973    .462332    -0.32   0.746    -1.056151    .7561569
            div9 |   .3629974   .6002647     0.60   0.545    -.8134998    1.539495
elevat_range_msa |  -.2014233   .0639313    -3.15   0.002    -.3267263   -.0761203
  ruggedness_msa |   13.70086   3.913744     3.50   0.000     6.030067    21.37166
      heating_dd |  -.0123367   .0055069    -2.24   0.025    -.0231299   -.0015434
      cooling_dd |  -.0131905   .0091851    -1.44   0.151     -.031193    .0048119
          sprawl |   .0049923   .0035646     1.40   0.161    -.0019942    .0119788
        _Iyear_2 |   .2393232   .0763485     3.13   0.002      .089683    .3889634
        _Iyear_3 |   .3398809   .1357522     2.50   0.012     .0738114    .6059504
           _cons |   14.76255   3.077934     4.80   0.000     8.729907    20.79519
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.280981   .1329575     9.63   0.000     1.020389    1.541573
           l_pop |   .1169112   .1056922     1.11   0.269    -.0902416    .3240641
   S_somecollege |   .9746795   .4192528     2.32   0.020     .1529592      1.7964
   l_mean_income |  -.7891745   .3859756    -2.04   0.041    -1.545673   -.0326763
          S_poor |  -.4226256   .6605455    -0.64   0.522    -1.717271    .8720198
         S_manuf |   .8891579   .3464497     2.57   0.010      .210129    1.568187
            div2 |  -.1330034   .1457964    -0.91   0.362    -.4187591    .1527522
            div3 |   .0782099   .1536999     0.51   0.611    -.2230364    .3794562
            div4 |  -.1307279   .1568986    -0.83   0.405    -.4382435    .1767878
            div5 |  -.2224041   .1674366    -1.33   0.184    -.5505737    .1057656
            div6 |  -.2748019   .1769193    -1.55   0.120    -.6215574    .0719535
            div7 |  -.1283665   .1951511    -0.66   0.511    -.5108557    .2541227
            div8 |  -.8119341   .2334897    -3.48   0.001    -1.269566   -.3543028
            div9 |  -.3672754    .218378    -1.68   0.093    -.7952884    .0607376
elevat_range_msa |  -.0420072   .0419508    -1.00   0.317    -.1242292    .0402148
  ruggedness_msa |   10.68226   2.256856     4.73   0.000     6.258903    15.10561
      heating_dd |  -.0183774   .0035996    -5.11   0.000    -.0254324   -.0113224
      cooling_dd |  -.0254568   .0077099    -3.30   0.001    -.0405679   -.0103456
          sprawl |   .0004822   .0027735     0.17   0.862    -.0049538    .0059182
        _Iyear_2 |   .3727348   .0826376     4.51   0.000     .2107681    .5347015
        _Iyear_3 |   .5081544   .1632347     3.11   0.002     .1882203    .8280885
           _cons |   13.46983   3.527713     3.82   0.000     6.555641    20.38402
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .8284553   .1448606     5.72   0.000     .5445338    1.112377
           l_pop |   .4427109   .1227767     3.61   0.000     .2020729    .6833488
   S_somecollege |   .8022959   .2485951     3.23   0.001     .3150584    1.289533
   l_mean_income |  -.6948332   .2239774    -3.10   0.002    -1.133821   -.2558456
          S_poor |  -.4742867   .4411031    -1.08   0.282    -1.338833    .3902594
         S_manuf |  -.1356275   .3155556    -0.43   0.667    -.7541051    .4828501
            div2 |   -.053704   .1219102    -0.44   0.660    -.2926436    .1852355
            div3 |   .2819839   .1285593     2.19   0.028     .0300122    .5339556
            div4 |   .1053642   .1433609     0.73   0.462     -.175618    .3863465
            div5 |   .0501832   .1337321     0.38   0.707    -.2119269    .3122933
            div6 |   .0088891   .1482909     0.06   0.952    -.2817556    .2995338
            div7 |   .0940854   .1458408     0.65   0.519    -.1917573    .3799281
            div8 |  -.1379344   .1677899    -0.82   0.411    -.4667965    .1909278
            div9 |   -.202351    .169452    -1.19   0.232    -.5344708    .1297689
elevat_range_msa |  -.0552776   .0479204    -1.15   0.249    -.1491997    .0386446
  ruggedness_msa |   7.844581   1.909473     4.11   0.000     4.102083    11.58708
      heating_dd |  -.0166075   .0031271    -5.31   0.000    -.0227366   -.0104785
      cooling_dd |  -.0291102   .0064622    -4.50   0.000    -.0417759   -.0164446
          sprawl |   .0020938   .0026857     0.78   0.436      -.00317    .0073576
        _Iyear_2 |   .2614821   .0675124     3.87   0.000     .1291602     .393804
        _Iyear_3 |   .3716086   .1135363     3.27   0.001     .1490814    .5941357
           _cons |   11.61228   2.231024     5.20   0.000     7.239554    15.98501
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8451573    .107796     7.84   0.000     .6338809    1.056434
           l_pop |   .3827608   .0848028     4.51   0.000     .2165503    .5489713
   S_somecollege |   .2897398   .2715275     1.07   0.286    -.2424442    .8219239
   l_mean_income |  -.1457435   .2366674    -0.62   0.538    -.6096031    .3181161
          S_poor |  -.3379673    .464472    -0.73   0.467    -1.248316    .5723811
         S_manuf |  -.2152116   .2844233    -0.76   0.449    -.7726709    .3422478
            div2 |  -.2914739   .1276962    -2.28   0.022    -.5417538    -.041194
            div3 |   .0163536   .1438243     0.11   0.909    -.2655368    .2982441
            div4 |  -.1736752   .1555273    -1.12   0.264     -.478503    .1311527
            div5 |  -.0987459   .1439234    -0.69   0.493    -.3808305    .1833387
            div6 |     -.1209   .1592987    -0.76   0.448    -.4331198    .1913198
            div7 |  -.1091898     .16137    -0.68   0.499    -.4254692    .2070896
            div8 |  -.2183399   .1968052    -1.11   0.267    -.6040709    .1673912
            div9 |  -.2527785   .2114265    -1.20   0.232     -.667167    .1616099
elevat_range_msa |  -.0714366   .0667356    -1.07   0.284     -.202236    .0593628
  ruggedness_msa |   7.195258   2.374361     3.03   0.002     2.541595    11.84892
      heating_dd |  -.0103676    .003187    -3.25   0.001     -.016614   -.0041213
      cooling_dd |  -.0158378   .0064372    -2.46   0.014    -.0284545   -.0032211
          sprawl |   .0006369   .0023407     0.27   0.786    -.0039508    .0052247
        _Iyear_2 |   .2905741   .0664461     4.37   0.000     .1603422     .420806
        _Iyear_3 |   .4469087   .1145676     3.90   0.000     .2223603    .6714572
           _cons |   6.804358   2.192812     3.10   0.002     2.506526    11.10219
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .7356465   .0877008     8.39   0.000     .5637561     .907537
           l_pop |   .4359441   .0701643     6.21   0.000     .2984246    .5734635
   S_somecollege |   1.131588   .2588846     4.37   0.000     .6241832    1.638992
   l_mean_income |    .032684   .2203617     0.15   0.882     -.399217     .464585
          S_poor |  -.6232694   .4013823    -1.55   0.120    -1.409964    .1634254
         S_manuf |   .2065197   .3972205     0.52   0.603    -.5720182    .9850577
            div2 |  -.2305917   .0683027    -3.38   0.001    -.3644625   -.0967208
            div3 |   .0539161   .0646788     0.83   0.405    -.0728519    .1806842
            div4 |  -.0968148   .0812302    -1.19   0.233    -.2560232    .0623935
            div5 |  -.0885891   .0951639    -0.93   0.352    -.2751069    .0979287
            div6 |  -.1413746   .0934615    -1.51   0.130    -.3245557    .0418065
            div7 |  -.1486866   .1007042    -1.48   0.140    -.3460631      .04869
            div8 |  -.2125826   .1064632    -2.00   0.046    -.4212467   -.0039185
            div9 |  -.2986376   .0912221    -3.27   0.001    -.4774296   -.1198455
elevat_range_msa |  -.0209062   .0359262    -0.58   0.561    -.0913202    .0495079
  ruggedness_msa |   4.484387   1.399501     3.20   0.001     1.741415    7.227359
      heating_dd |  -.0068609   .0026078    -2.63   0.009     -.011972   -.0017498
      cooling_dd |   -.000323   .0078761    -0.04   0.967    -.0157599    .0151139
          sprawl |    .004087   .0017205     2.38   0.018      .000715    .0074591
        _Iyear_2 |   .1702311   .0619148     2.75   0.006     .0488804    .2915818
        _Iyear_3 |   .2320577   .1152877     2.01   0.044     .0060981    .4580174
           _cons |   4.541328   2.118519     2.14   0.032      .389108    8.693549
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            9.591                 2.697
Constant effect |            4.160                 2.332
Dominance       |            0.000                 2.826
Exogeneity      |            2.904                 2.529
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            9.591                 2.411
Constant effect |            4.160                 2.098
Dominance       |            0.000                 2.340
Exogeneity      |            2.904                 2.178
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_rail1898 ), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   14310.83
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9507
                                                  Root MSE        =     .30399

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.022088    .137705     7.42   0.000     .7521909    1.291985
           l_pop |   .2417284   .1371422     1.76   0.078    -.0270654    .5105223
         l_pop80 |   .6795412   .2874029     2.36   0.018     .1162419     1.24284
         l_pop70 |  -.2084247   .3650167    -0.57   0.568    -.9238443    .5069949
         l_pop60 |  -.4785935   .3091629    -1.55   0.122    -1.084542    .1273547
         l_pop50 |  -.0469247   .1911333    -0.25   0.806    -.4215391    .3276898
         l_pop40 |   .0158032   .2037481     0.08   0.938    -.3835358    .4151423
         l_pop30 |    .134418   .1695554     0.79   0.428    -.1979044    .4667405
         l_pop20 |  -.0015502    .084959    -0.02   0.985    -.1680668    .1649664
   S_somecollege |   .6136215   .3180797     1.93   0.054    -.0098033    1.237046
   l_mean_income |  -.5981991   .2110945    -2.83   0.005    -1.011937   -.1844615
          S_poor |  -.3296387   .3546488    -0.93   0.353    -1.024738    .3654601
         S_manuf |    .445196   .3243965     1.37   0.170    -.1906094    1.081001
            div2 |  -.1355561   .0778408    -1.74   0.082    -.2881212     .017009
            div3 |    .087299   .1070936     0.82   0.415    -.1226006    .2971987
            div4 |   -.031612   .1060992    -0.30   0.766    -.2395627    .1763386
            div5 |  -.1168969   .1016191    -1.15   0.250    -.3160667    .0822729
            div6 |  -.1941663   .1198491    -1.62   0.105    -.4290663    .0407337
            div7 |  -.0608424   .1184282    -0.51   0.607    -.2929574    .1712727
            div8 |  -.3632085   .1713468    -2.12   0.034     -.699042    -.027375
            div9 |  -.2587489   .1452377    -1.78   0.075    -.5434096    .0259118
elevat_range_msa |  -.0571508   .0308501    -1.85   0.064     -.117616    .0033143
  ruggedness_msa |   5.030591   1.922496     2.62   0.009     1.262568    8.798614
      heating_dd |  -.0156554    .002769    -5.65   0.000    -.0210826   -.0102282
      cooling_dd |  -.0283085   .0065843    -4.30   0.000    -.0412136   -.0154035
          sprawl |   .0013379   .0022657     0.59   0.555    -.0031028    .0057785
        _Iyear_2 |   .3382951   .0590051     5.73   0.000     .2226472     .453943
        _Iyear_3 |   .5206866   .1135378     4.59   0.000     .2981566    .7432165
           _cons |    10.7029   1.910158     5.60   0.000     6.959062    14.44674
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 xi: ivqregress iqr l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_rail1898 ) ,  quantile(10 25 50 75 90) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(140) = 44358.33
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.344017    .218614     6.15   0.000      .915542    1.772493
           l_pop |   .3145568    .136073     2.31   0.021     .0478586     .581255
         l_pop80 |   .1772783   .4954493     0.36   0.720    -.7937844    1.148341
         l_pop70 |   .3155157   .6516905     0.48   0.628    -.9617743    1.592806
         l_pop60 |  -.9248588   .6163252    -1.50   0.133    -2.132834    .2831165
         l_pop50 |   .0771456   .4561097     0.17   0.866     -.816813    .9711042
         l_pop40 |     .43159   .2611775     1.65   0.098    -.0803086    .9434886
         l_pop30 |  -.1889027   .2824207    -0.67   0.504    -.7424372    .3646317
         l_pop20 |  -.0816851   .1322967    -0.62   0.537    -.3409818    .1776117
   S_somecollege |   .8828535   .6175992     1.43   0.153    -.3276186    2.093326
   l_mean_income |  -.9241581   .4567916    -2.02   0.043    -1.819453   -.0288631
          S_poor |  -.6360894   .6013823    -1.06   0.290    -1.814777    .5425982
         S_manuf |   .7832779     .34465     2.27   0.023     .1077763    1.458779
            div2 |   .2151245   .4086121     0.53   0.599    -.5857406     1.01599
            div3 |   .3401839   .4420975     0.77   0.442    -.5263112    1.206679
            div4 |   .2375525   .4153351     0.57   0.567    -.5764893    1.051594
            div5 |   .1539666   .4881523     0.32   0.752    -.8027943    1.110728
            div6 |   .1643881   .4605753     0.36   0.721    -.7383228    1.067099
            div7 |   .0923051   .4967105     0.19   0.853    -.8812296     1.06584
            div8 |  -.3616615   .4361476    -0.83   0.407    -1.216495    .4931722
            div9 |   .1898983   .5810136     0.33   0.744    -.9488674    1.328664
elevat_range_msa |  -.1607981   .0776354    -2.07   0.038    -.3129607   -.0086354
  ruggedness_msa |   8.864501   5.135537     1.73   0.084    -1.200968    18.92997
      heating_dd |   -.015898   .0044469    -3.58   0.000    -.0246137   -.0071823
      cooling_dd |  -.0281896   .0099581    -2.83   0.005     -.047707   -.0086722
          sprawl |   .0027541    .003818     0.72   0.471    -.0047291    .0102373
        _Iyear_2 |   .2692413   .1023412     2.63   0.009     .0686563    .4698263
        _Iyear_3 |   .4083448   .1887492     2.16   0.031     .0384032    .7782864
           _cons |   13.74027   4.096114     3.35   0.001     5.712034     21.7685
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.179065   .1669656     7.06   0.000     .8518187    1.506312
           l_pop |   .2066289   .1630365     1.27   0.205    -.1129168    .5261747
         l_pop80 |   .5684646   .3459389     1.64   0.100    -.1095632    1.246492
         l_pop70 |   .0847122   .4164314     0.20   0.839    -.7314783    .9009028
         l_pop60 |  -.9657854   .5067751    -1.91   0.057    -1.959046    .0274755
         l_pop50 |   .1080245   .3763596     0.29   0.774    -.6296268    .8456758
         l_pop40 |   .2284476   .2743548     0.83   0.405    -.3092778    .7661731
         l_pop30 |  -.0941001   .2172826    -0.43   0.665    -.5199662     .331766
         l_pop20 |   .0535862   .1110936     0.48   0.630    -.1641532    .2713257
   S_somecollege |   .2964181   .5864084     0.51   0.613    -.8529211    1.445757
   l_mean_income |  -.4701247   .4078541    -1.15   0.249    -1.269504    .3292546
          S_poor |  -.6077459    .582683    -1.04   0.297    -1.749784    .5342919
         S_manuf |   .4388575   .3295644     1.33   0.183    -.2070768    1.084792
            div2 |  -.0839816   .1867571    -0.45   0.653    -.4500188    .2820556
            div3 |   .0905155   .1887374     0.48   0.632    -.2794031    .4604341
            div4 |  -.0305856   .1780695    -0.17   0.864    -.3795954    .3184243
            div5 |  -.1678647   .1859725    -0.90   0.367     -.532364    .1966347
            div6 |  -.2459384   .2111759    -1.16   0.244    -.6598355    .1679587
            div7 |  -.1059454   .2346715    -0.45   0.652    -.5658932    .3540024
            div8 |  -.7018458   .3093933    -2.27   0.023    -1.308246   -.0954461
            div9 |  -.3195636   .2885491    -1.11   0.268    -.8851095    .2459822
elevat_range_msa |  -.0683261   .0509016    -1.34   0.179    -.1680915    .0314392
  ruggedness_msa |   8.747991   4.638471     1.89   0.059    -.3432452    17.83923
      heating_dd |   -.021182   .0049191    -4.31   0.000    -.0308232   -.0115407
      cooling_dd |  -.0356521   .0111649    -3.19   0.001     -.057535   -.0137692
          sprawl |   .0005811   .0039735     0.15   0.884    -.0072068    .0083689
        _Iyear_2 |    .385819   .0913496     4.22   0.000     .2067769     .564861
        _Iyear_3 |   .5454872   .1703398     3.20   0.001     .2116273    .8793471
           _cons |   10.54698   3.274285     3.22   0.001       4.1295    16.96446
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .8674519   .1544604     5.62   0.000     .5647151    1.170189
           l_pop |     .24543   .1994071     1.23   0.218    -.1454007    .6362606
         l_pop80 |   .7828165   .5053179     1.55   0.121    -.2075884    1.773221
         l_pop70 |  -.3138305    .669633    -0.47   0.639    -1.626287    .9986262
         l_pop60 |  -.3777164   .5389264    -0.70   0.483    -1.433993    .6785599
         l_pop50 |  -.0966755   .2902902    -0.33   0.739    -.6656338    .4722828
         l_pop40 |   .1268637   .2285196     0.56   0.579    -.3210266     .574754
         l_pop30 |   .0727887   .2269724     0.32   0.748     -.372069    .5176465
         l_pop20 |  -.0126712   .0996577    -0.13   0.899    -.2079967    .1826542
   S_somecollege |   .3888624   .5374183     0.72   0.469    -.6644582    1.442183
   l_mean_income |  -.7103498   .2430126    -2.92   0.003    -1.186646   -.2340538
          S_poor |  -.4022877   .5249259    -0.77   0.443    -1.431124    .6265482
         S_manuf |  -.2001119   .2759257    -0.73   0.468    -.7409163    .3406926
            div2 |  -.0858365   .1418428    -0.61   0.545    -.3638433    .1921703
            div3 |   .2016966   .1485579     1.36   0.175    -.0894716    .4928648
            div4 |   .0883868   .1594378     0.55   0.579    -.2241057    .4008792
            div5 |  -.0823649    .166024    -0.50   0.620     -.407766    .2430362
            div6 |  -.0688692   .1799624    -0.38   0.702     -.421589    .2838507
            div7 |  -.0256485   .1688491    -0.15   0.879    -.3565867    .3052897
            div8 |  -.3219006   .2295276    -1.40   0.161    -.7717664    .1279653
            div9 |  -.3160014    .254389    -1.24   0.214    -.8145947    .1825918
elevat_range_msa |  -.0298953   .0464601    -0.64   0.520    -.1209553    .0611648
  ruggedness_msa |    6.19589   2.397112     2.58   0.010     1.497637    10.89414
      heating_dd |  -.0179642   .0041179    -4.36   0.000    -.0260352   -.0098932
      cooling_dd |  -.0341546   .0098482    -3.47   0.001    -.0534568   -.0148524
          sprawl |   .0004319   .0028763     0.15   0.881    -.0052056    .0060694
        _Iyear_2 |   .3066363    .099507     3.08   0.002     .1116062    .5016664
        _Iyear_3 |    .481856   .1699179     2.84   0.005      .148823    .8148889
           _cons |   12.05307   2.533474     4.76   0.000     7.087548    17.01858
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8366556   .1898194     4.41   0.000     .4646164    1.208695
           l_pop |   .2779267   .2526308     1.10   0.271    -.2172206    .7730741
         l_pop80 |   .3971503   .6756846     0.59   0.557    -.9271673    1.721468
         l_pop70 |  -.0351781   .8802808    -0.04   0.968    -1.760497    1.690141
         l_pop60 |  -.3111923   .5950206    -0.52   0.601    -1.477411    .8550267
         l_pop50 |  -.1043422   .3707616    -0.28   0.778    -.8310216    .6223372
         l_pop40 |   .0825238   .4803815     0.17   0.864    -.8590067    1.024054
         l_pop30 |   .1146211   .3017352     0.38   0.704     -.476769    .7060112
         l_pop20 |    -.00786   .1413192    -0.06   0.956    -.2848405    .2691205
   S_somecollege |   .3982092   .7088898     0.56   0.574    -.9911892    1.787608
   l_mean_income |  -.0508813   .3821744    -0.13   0.894    -.7999294    .6981668
          S_poor |  -.0220578   .7495226    -0.03   0.977    -1.491095     1.44698
         S_manuf |  -.2558072   .4124659    -0.62   0.535    -1.064225    .5526112
            div2 |  -.2605425   .0986663    -2.64   0.008    -.4539249   -.0671601
            div3 |   .0323266   .0895241     0.36   0.718    -.1431373    .2077905
            div4 |  -.1457712   .1111079    -1.31   0.190    -.3635386    .0719962
            div5 |   -.078978   .1241119    -0.64   0.525    -.3222329     .164277
            div6 |    -.12865   .1239839    -1.04   0.299    -.3716539     .114354
            div7 |  -.0980051   .1528718    -0.64   0.521    -.3976283     .201618
            div8 |  -.1409415   .2267962    -0.62   0.534    -.5854538    .3035708
            div9 |  -.1709347    .151621    -1.13   0.260    -.4681065     .126237
elevat_range_msa |  -.0761938   .0592906    -1.29   0.199    -.1924013    .0400136
  ruggedness_msa |   5.300952   3.073967     1.72   0.085    -.7239127    11.32582
      heating_dd |   -.010802     .00577    -1.87   0.061     -.022111    .0005071
      cooling_dd |  -.0168311    .014924    -1.13   0.259    -.0460815    .0124193
          sprawl |   .0016274    .003379     0.48   0.630    -.0049954    .0082501
        _Iyear_2 |    .300143   .1454899     2.06   0.039     .0149881     .585298
        _Iyear_3 |   .4872275   .2709998     1.80   0.072    -.0439224    1.018377
           _cons |   5.636771   3.548902     1.59   0.112    -1.318949    12.59249
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .5277268   .1517448     3.48   0.001     .2303125    .8251412
           l_pop |   .6220215   .1598816     3.89   0.000     .3086594    .9353836
         l_pop80 |   .2586824   .3381469     0.76   0.444    -.4040734    .9214382
         l_pop70 |  -.2159719   .4323005    -0.50   0.617    -1.063265    .6313215
         l_pop60 |   -.101554   .3438298    -0.30   0.768     -.775448      .57234
         l_pop50 |   -.206494   .1772122    -1.17   0.244    -.5538235    .1408356
         l_pop40 |   .2478892   .2118744     1.17   0.242     -.167377    .6631555
         l_pop30 |  -.1484442   .1597918    -0.93   0.353    -.4616304     .164742
         l_pop20 |   .1726236   .1007466     1.71   0.087    -.0248361    .3700834
   S_somecollege |   .8866443   .4489839     1.97   0.048     .0066521    1.766636
   l_mean_income |  -.0484267   .2377827    -0.20   0.839    -.5144722    .4176188
          S_poor |  -.6183143   .5295551    -1.17   0.243    -1.656223    .4195946
         S_manuf |  -.1198673   .4654792    -0.26   0.797     -1.03219    .7924552
            div2 |  -.1757446   .0715529    -2.46   0.014    -.3159857   -.0355036
            div3 |    .075768   .0794609     0.95   0.340    -.0799724    .2315085
            div4 |   .0101057   .0969772     0.10   0.917    -.1799661    .2001774
            div5 |   -.148843   .0676855    -2.20   0.028    -.2815041   -.0161819
            div6 |  -.1871287   .0727127    -2.57   0.010    -.3296429   -.0446145
            div7 |  -.0986516   .0844085    -1.17   0.243    -.2640892    .0667861
            div8 |   -.125952   .1646536    -0.76   0.444    -.4486673    .1967632
            div9 |  -.2656089   .1046038    -2.54   0.011    -.4706285   -.0605893
elevat_range_msa |   -.036246   .0402638    -0.90   0.368    -.1151617    .0426696
  ruggedness_msa |   4.372976   1.724909     2.54   0.011      .992216    7.753736
      heating_dd |   -.010954   .0026591    -4.12   0.000    -.0161657   -.0057423
      cooling_dd |  -.0064924   .0059732    -1.09   0.277    -.0181997    .0052149
          sprawl |   .0053738   .0023878     2.25   0.024     .0006937    .0100538
        _Iyear_2 |   .1787567   .1007975     1.77   0.076    -.0188029    .3763163
        _Iyear_3 |   .2461529   .1857695     1.33   0.185    -.1179487    .6102546
           _cons |    4.61808   2.154183     2.14   0.032     .3959597    8.840201
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            6.921                 2.555
Constant effect |            3.205                 2.386
Dominance       |            0.000                 2.379
Exogeneity      |            1.755                 2.283
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            6.921                 2.306
Constant effect |            3.205                 2.004
Dominance       |            0.000                 2.045
Exogeneity      |            1.755                 2.002
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
.         
.         
. * Fourth Panel *        
.                 
.                 local Inst "l_pix1835"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_pix1835 ), robust    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    1034.56
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8770
                                                  Root MSE        =     .48009

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.249547   .0472133    26.47   0.000      1.15701    1.342083
    _Iyear_2 |   .4047518   .0464308     8.72   0.000     .3137492    .4957544
    _Iyear_3 |   .6739672   .0457711    14.72   0.000     .5842575    .7636769
       _cons |   6.709531   .3121219    21.50   0.000     6.097784    7.321279
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_pix1835

. 
.                 xi: ivqregress iqr l_vmt i.year (l_ln =  l_pix1835 ) ,  quantile(10
>  25 50 75 90) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     684
Estimator: Inverse quantile regression                 Wald chi2(15) = 3078.18
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.508974   .0594944    25.36   0.000     1.392367    1.625581
    _Iyear_2 |   .3744388   .1253204     2.99   0.003     .1288154    .6200623
    _Iyear_3 |   .6548716   .1159615     5.65   0.000     .4275913     .882152
       _cons |   4.359026   .4564095     9.55   0.000      3.46448    5.253572
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.352115   .0558171    24.22   0.000     1.242715    1.461514
    _Iyear_2 |   .4341201   .0739963     5.87   0.000     .2890901    .5791501
    _Iyear_3 |   .7033642   .0795789     8.84   0.000     .5473923     .859336
       _cons |   5.733973   .3603989    15.91   0.000     5.027604    6.440342
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.262375   .0319301    39.54   0.000     1.199793    1.324956
    _Iyear_2 |   .4472794   .0440465    10.15   0.000     .3609499    .5336089
    _Iyear_3 |   .6746584   .0468723    14.39   0.000     .5827904    .7665264
       _cons |   6.687287   .2213507    30.21   0.000     6.253447    7.121126
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.177436    .027534    42.76   0.000     1.123471    1.231402
    _Iyear_2 |   .3591321   .0460248     7.80   0.000     .2689252     .449339
    _Iyear_3 |   .6190996   .0446607    13.86   0.000     .5315662     .706633
       _cons |   7.513424   .1991548    37.73   0.000     7.123088     7.90376
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.076211   .0372035    28.93   0.000     1.003294    1.149129
    _Iyear_2 |   .3308455   .0567881     5.83   0.000     .2195428    .4421483
    _Iyear_3 |   .5551481   .0546538    10.16   0.000     .4480286    .6622676
       _cons |   8.461877   .2881556    29.37   0.000     7.897102    9.026651
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           41.671                 2.371
Constant effect |            6.488                 2.260
Dominance       |            0.000                 2.583
Exogeneity      |            2.607                 2.730
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           41.671                 2.156
Constant effect |            6.488                 2.026
Dominance       |            0.000                 2.166
Exogeneity      |            2.607                 2.284
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year (l_ln =  l_pix1835 ), robust 
>  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7811.79
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9289
                                                  Root MSE        =     .36496

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .6336354   .1105657     5.73   0.000     .4169306    .8503402
       l_pop |    .605914   .0754651     8.03   0.000     .4580051     .753823
    _Iyear_2 |   .4006095    .036133    11.09   0.000     .3297901    .4714289
    _Iyear_3 |   .6239473   .0362381    17.22   0.000     .5529219    .6949727
       _cons |   3.000334   .2741643    10.94   0.000     2.462982    3.537687
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_pix1835

. 
.                 xi: ivqregress iqr l_vmt l_pop i.year (l_ln =  l_pix1835 ) ,  quant
> ile(10 25 50 75 90) bound(-2 1.5) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(20) = 13664.61
                                                      Prob > chi2   =   0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.087158   .1617446     6.72   0.000     .7701445    1.404172
       l_pop |   .4005881   .1062138     3.77   0.000     .1924128    .6087634
    _Iyear_2 |    .319061   .0281433    11.34   0.000     .2639012    .3742208
    _Iyear_3 |   .5529805   .0290248    19.05   0.000      .496093    .6098681
       _cons |   2.615305   .3309443     7.90   0.000     1.966666    3.263944
-------------+----------------------------------------------------------------
q25          |
        l_ln |   .8541023   .0960267     8.89   0.000     .6658934    1.042311
       l_pop |   .4706917   .0710721     6.62   0.000     .3313929    .6099905
    _Iyear_2 |    .429024   .0437274     9.81   0.000       .34332    .5147281
    _Iyear_3 |   .6505903   .0432777    15.03   0.000     .5657675    .7354131
       _cons |   3.129361   .3028493    10.33   0.000     2.535787    3.722935
-------------+----------------------------------------------------------------
q50          |
        l_ln |   .5330955   .1386083     3.85   0.000     .2614283    .8047628
       l_pop |   .7035176   .1109953     6.34   0.000     .4859708    .9210643
    _Iyear_2 |   .3721126    .051105     7.28   0.000     .2719486    .4722765
    _Iyear_3 |   .5946846   .0505382    11.77   0.000     .4956315    .6937377
       _cons |   2.651448   .5389787     4.92   0.000     1.595069    3.707827
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .3416568   .1104197     3.09   0.002     .1252381    .5580754
       l_pop |   .7750408   .0760955    10.19   0.000     .6258963    .9241853
    _Iyear_2 |   .3470651   .0403406     8.60   0.000      .267999    .4261311
    _Iyear_3 |   .5637924   .0428018    13.17   0.000     .4799023    .6476824
       _cons |   2.924329   .2798677    10.45   0.000     2.375798    3.472859
-------------+----------------------------------------------------------------
q90          |
        l_ln |    .345541   .2079912     1.66   0.097    -.0621141    .7531962
       l_pop |   .7428361   .1611947     4.61   0.000     .4269004    1.058772
    _Iyear_2 |   .2959746   .0450917     6.56   0.000     .2075964    .3843528
    _Iyear_3 |   .5077787   .0423181    12.00   0.000     .4248368    .5907207
       _cons |   3.665109   .6778285     5.41   0.000     2.336589    4.993628
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            8.191                 2.506
Constant effect |            2.599                 2.047
Dominance       |            0.000                 2.121
Exogeneity      |            3.467                 2.497
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            8.191                 2.311
Constant effect |            2.599                 1.847
Dominance       |            0.000                 1.984
Exogeneity      |            3.467                 2.217
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _pix1835 ), robust                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   14897.34
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9479
                                                  Root MSE        =     .31254

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .7468345   .1171014     6.38   0.000     .5173201     .976349
           l_pop |   .5286634   .0922629     5.73   0.000     .3478313    .7094955
            div2 |  -.1215993   .0883278    -1.38   0.169    -.2947185      .05152
            div3 |   .1646094   .0986984     1.67   0.095    -.0288358    .3580547
            div4 |   .1260928   .1198589     1.05   0.293    -.1088262    .3610119
            div5 |    .061736   .1169414     0.53   0.598    -.1674649    .2909369
            div6 |   .0024044   .1283123     0.02   0.985    -.2490832     .253892
            div7 |   .0363753   .1295263     0.28   0.779    -.2174917    .2902422
            div8 |  -.0646132   .1652396    -0.39   0.696    -.3884767    .2592504
            div9 |  -.1421846   .1423669    -1.00   0.318    -.4212186    .1368495
elevat_range_msa |  -.0350281   .0310709    -1.13   0.260    -.0959259    .0258697
  ruggedness_msa |   5.847751   1.800376     3.25   0.001     2.319079    9.376423
      heating_dd |  -.0114928   .0024533    -4.68   0.000    -.0163012   -.0066844
      cooling_dd |  -.0171256   .0048317    -3.54   0.000    -.0265956   -.0076556
          sprawl |   .0050671   .0019119     2.65   0.008     .0013199    .0088144
        _Iyear_2 |    .394952   .0306331    12.89   0.000     .3349123    .4549918
        _Iyear_3 |    .622516   .0314283    19.81   0.000     .5609176    .6841143
           _cons |   3.729259   .6718954     5.55   0.000     2.412368    5.046149
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year (l_ln =  l_
> pix1835 ) ,  quantile(10 25 50 75 90) bound(-1 3.5, at(0.9)) vce(robust)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      684
Estimator: Inverse quantile regression                Wald chi2(85) = 19537.55
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.615366    .448706     3.60   0.000     .7359184    2.494814
           l_pop |  -.0921491   .3227577    -0.29   0.775    -.7247426    .5404445
            div2 |   .1055099   .2754902     0.38   0.702     -.434441    .6454608
            div3 |   .2386068   .2947307     0.81   0.418    -.3390547    .8162683
            div4 |   .1097358   .3480799     0.32   0.753    -.5724884    .7919599
            div5 |   .0698682   .3231449     0.22   0.829    -.5634841    .7032205
            div6 |   .0798387   .3258177     0.25   0.806    -.5587522    .7184296
            div7 |   .0332081   .3390207     0.10   0.922    -.6312603    .6976765
            div8 |  -.8310345   .5542393    -1.50   0.134    -1.917324    .2552546
            div9 |  -.3054666   .3884403    -0.79   0.432    -1.066796    .4558625
elevat_range_msa |  -.0853708   .0491659    -1.74   0.082    -.1817342    .0109927
  ruggedness_msa |   17.17664   5.718237     3.00   0.003     5.969107    28.38418
      heating_dd |  -.0260568   .0061662    -4.23   0.000    -.0381424   -.0139712
      cooling_dd |  -.0398622   .0095242    -4.19   0.000    -.0585293    -.021195
          sprawl |  -.0033028   .0039158    -0.84   0.399    -.0109776    .0043719
        _Iyear_2 |   .4246826   .0466471     9.10   0.000      .333256    .5161093
        _Iyear_3 |   .6436912   .0507544    12.68   0.000     .5442144    .7431681
           _cons |     7.0049   1.691348     4.14   0.000      3.68992    10.31988
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   .8450932   .2244627     3.76   0.000     .4051543    1.285032
           l_pop |   .4771446   .1895483     2.52   0.012     .1056368    .8486524
            div2 |  -.0590838   .1133225    -0.52   0.602    -.2811918    .1630243
            div3 |   .1453659   .1181232     1.23   0.218    -.0861513    .3768831
            div4 |   .1661371   .1469447     1.13   0.258    -.1218693    .4541434
            div5 |  -.0085375   .1746814    -0.05   0.961    -.3509068    .3338319
            div6 |  -.0447556   .2109174    -0.21   0.832    -.4581462    .3686349
            div7 |  -.0104808   .1733891    -0.06   0.952    -.3503172    .3293557
            div8 |  -.2711393   .3468347    -0.78   0.434    -.9509228    .4086443
            div9 |  -.2521309   .2090131    -1.21   0.228    -.6617891    .1575273
elevat_range_msa |  -.0525076   .0633822    -0.83   0.407    -.1767344    .0717192
  ruggedness_msa |   9.582921   2.097184     4.57   0.000     5.472516    13.69333
      heating_dd |  -.0164663   .0045308    -3.63   0.000    -.0253465    -.007586
      cooling_dd |  -.0267309   .0074138    -3.61   0.000    -.0412617   -.0122001
          sprawl |   .0033649   .0019124     1.76   0.078    -.0003833    .0071131
        _Iyear_2 |   .4191967   .0337815    12.41   0.000     .3529861    .4854073
        _Iyear_3 |    .643976      .0432    14.91   0.000     .5593056    .7286464
           _cons |   4.258199   1.273391     3.34   0.001     1.762398       6.754
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .7499217   .1289383     5.82   0.000     .4972073    1.002636
           l_pop |   .5007865    .109296     4.58   0.000     .2865703    .7150027
            div2 |  -.1252584    .093028    -1.35   0.178    -.3075899     .057073
            div3 |   .1531271   .0991434     1.54   0.122    -.0411903    .3474445
            div4 |   .0709974   .0988412     0.72   0.473    -.1227278    .2647226
            div5 |   .0002763   .1057122     0.00   0.998    -.2069158    .2074683
            div6 |   .0164724   .1133397     0.15   0.884    -.2056693    .2386142
            div7 |  -.0186013   .1133435    -0.16   0.870    -.2407504    .2035479
            div8 |  -.1681603   .1499263    -1.12   0.262    -.4620103    .1256898
            div9 |  -.2622536   .1511675    -1.73   0.083    -.5585364    .0340292
elevat_range_msa |  -.0343269   .0545853    -0.63   0.529    -.1413122    .0726583
  ruggedness_msa |   6.698917   2.192629     3.06   0.002     2.401443    10.99639
      heating_dd |  -.0155279   .0026124    -5.94   0.000    -.0206481   -.0104076
      cooling_dd |  -.0250083   .0066728    -3.75   0.000    -.0380867   -.0119299
          sprawl |   .0009073   .0020491     0.44   0.658     -.003109    .0049235
        _Iyear_2 |   .3846054   .0323146    11.90   0.000       .32127    .4479408
        _Iyear_3 |   .5956497   .0350492    16.99   0.000     .5269545    .6643449
           _cons |   4.664805   .6565372     7.11   0.000     3.378015    5.951594
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .6728682   .1143471     5.88   0.000      .448752    .8969844
           l_pop |   .5464168   .0929038     5.88   0.000     .3643288    .7285048
            div2 |  -.3271962   .0804383    -4.07   0.000    -.4848523   -.1695401
            div3 |    .004955   .0789232     0.06   0.950    -.1497316    .1596416
            div4 |  -.1260397   .1184403    -1.06   0.287    -.3581783    .1060989
            div5 |  -.1504632   .1089443    -1.38   0.167    -.3639901    .0630636
            div6 |  -.1460973   .0955982    -1.53   0.126    -.3334663    .0412717
            div7 |   -.101676   .1147187    -0.89   0.375    -.3265204    .1231685
            div8 |  -.2498674   .1627621    -1.54   0.125    -.5688753    .0691405
            div9 |  -.3552072   .1386251    -2.56   0.010    -.6269074   -.0835069
elevat_range_msa |  -.0060644   .0593293    -0.10   0.919    -.1223476    .1102188
  ruggedness_msa |   6.280355   2.597381     2.42   0.016     1.189583    11.37113
      heating_dd |  -.0098455   .0036295    -2.71   0.007    -.0169591   -.0027319
      cooling_dd |  -.0108542   .0075444    -1.44   0.150    -.0256409    .0039324
          sprawl |   .0019791   .0031678     0.62   0.532    -.0042297    .0081879
        _Iyear_2 |   .3771798   .0427708     8.82   0.000     .2933505     .461009
        _Iyear_3 |   .5778806   .0469194    12.32   0.000     .4859202     .669841
           _cons |   4.417704   .6555921     6.74   0.000     3.132767     5.70264
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .6330559   .1133253     5.59   0.000     .4109423    .8551695
           l_pop |   .5759497    .095515     6.03   0.000     .3887437    .7631557
            div2 |  -.2614119   .0675329    -3.87   0.000    -.3937741   -.1290498
            div3 |   .0251028    .061475     0.41   0.683    -.0953861    .1455916
            div4 |   .0409464   .1131523     0.36   0.717     -.180828    .2627207
            div5 |  -.0961368    .111232    -0.86   0.387    -.3141475     .121874
            div6 |  -.1576768   .0992929    -1.59   0.112    -.3522872    .0369337
            div7 |  -.1125184   .1277601    -0.88   0.378    -.3629236    .1378868
            div8 |  -.0651898   .1330528    -0.49   0.624    -.3259685    .1955888
            div9 |  -.2426469   .0960608    -2.53   0.012    -.4309227   -.0543712
elevat_range_msa |   -.009443   .0439115    -0.22   0.830    -.0955081     .076622
  ruggedness_msa |   4.237393    1.72512     2.46   0.014       .85622    7.618567
      heating_dd |  -.0070626   .0028478    -2.48   0.013    -.0126442    -.001481
      cooling_dd |  -.0029473   .0084463    -0.35   0.727    -.0195017    .0136071
          sprawl |   .0058227      .0023     2.53   0.011     .0013147    .0103307
        _Iyear_2 |   .3132182   .0332663     9.42   0.000     .2480175    .3784189
        _Iyear_3 |   .5258769   .0352248    14.93   0.000     .4568376    .5949163
           _cons |   3.967182   .6101444     6.50   0.000     2.771321    5.163043
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            6.759                 2.324
Constant effect |            1.386                 2.233
Dominance       |            0.000                 2.410
Exogeneity      |            1.221                 2.762
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            6.759                 2.110
Constant effect |            1.386                 2.075
Dominance       |            0.000                 2.186
Exogeneity      |            1.221                 2.377
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_pix1835 ), robust                       
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   15229.17
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9476
                                                  Root MSE        =     .31346

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .6772969   .1334154     5.08   0.000     .4158076    .9387862
           l_pop |   .6091094   .1044057     5.83   0.000     .4044781    .8137408
   S_somecollege |   1.054366   .2085277     5.06   0.000     .6456594    1.463073
   l_mean_income |  -.9114907   .2249557    -4.05   0.000    -1.352396   -.4705856
          S_poor |  -.5755428   .3428252    -1.68   0.093    -1.247468    .0963821
         S_manuf |  -.2003849    .346474    -0.58   0.563    -.8794614    .4786916
            div2 |  -.0377521   .1056365    -0.36   0.721    -.2447959    .1692917
            div3 |   .2962848   .1288286     2.30   0.021     .0437854    .5487843
            div4 |   .1734115   .1364845     1.27   0.204    -.0940932    .4409163
            div5 |   .0551612   .1271769     0.43   0.664    -.1941009    .3044232
            div6 |   .0395777   .1447786     0.27   0.785    -.2441831    .3233385
            div7 |   .1234567   .1497828     0.82   0.410    -.1701122    .4170256
            div8 |   .0185451   .1754118     0.11   0.916    -.3252557    .3623459
            div9 |  -.0659656   .1499259    -0.44   0.660    -.3598149    .2278837
elevat_range_msa |  -.0587405   .0285293    -2.06   0.039    -.1146568   -.0028241
  ruggedness_msa |   5.386386   1.791033     3.01   0.003     1.876025    8.896747
      heating_dd |  -.0118592   .0023088    -5.14   0.000    -.0163844   -.0073339
      cooling_dd |  -.0190494   .0051207    -3.72   0.000    -.0290857    -.009013
          sprawl |   .0068105   .0022156     3.07   0.002     .0024681    .0111529
        _Iyear_2 |   .2323909   .0506287     4.59   0.000     .1331604    .3316214
        _Iyear_3 |   .2958188   .0918686     3.22   0.001     .1157596    .4758781
           _cons |   11.98043   2.084586     5.75   0.000     7.894712    16.06614
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_pix1835 ) ,  quantile(10 25 50 75 90) bo
> und(0 1, at(0.1)) vce(robust, kernel(gaussian) bwidth(bofinger)) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     684
Estimator: Inverse quantile regression                 Wald chi2(86) = 7623.47
                                                       Prob > chi2   =  0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   .4415379          .        .       .            .           .
           l_pop |   .2703931          .        .       .            .           .
   S_somecollege |   1.774411          .        .       .            .           .
   l_mean_income |  -1.095271          .        .       .            .           .
          S_poor |  -.5124318          .        .       .            .           .
         S_manuf |   1.364108          .        .       .            .           .
            div2 |  -.2639306          .        .       .            .           .
            div3 |   .1380244          .        .       .            .           .
            div4 |  -.0589681          .        .       .            .           .
            div5 |  -.1968348          .        .       .            .           .
            div6 |   -.240168          .        .       .            .           .
            div7 |  -.1607506          .        .       .            .           .
            div8 |  -.4078612          .        .       .            .           .
            div9 |  -.3228689          .        .       .            .           .
elevat_range_msa |  -.0374966          .        .       .            .           .
  ruggedness_msa |   11.56935          .        .       .            .           .
      heating_dd |  -.0152453   2.10e+14    -0.00   1.000    -4.12e+14    4.12e+14
      cooling_dd |  -.0200748   1.68e+15    -0.00   1.000    -3.29e+15    3.29e+15
          sprawl |   .0013365   1.20e+14     0.00   1.000    -2.35e+14    2.35e+14
        _Iyear_2 |   .2977188          .        .       .            .           .
        _Iyear_3 |   .3598183          .        .       .            .           .
           _cons |   14.37625          .        .       .            .           .
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   .9054879   1.431224     0.63   0.527     -1.89966    3.710636
           l_pop |   .3812872   1.206034     0.32   0.752    -1.982497    2.745071
   S_somecollege |   1.509593   1.929426     0.78   0.434    -2.272012    5.291198
   l_mean_income |  -1.122621   1.817892    -0.62   0.537    -4.685623    2.440381
          S_poor |  -1.368644   1.083483    -1.26   0.207    -3.492231    .7549436
         S_manuf |   .3870257   1.639176     0.24   0.813    -2.825701    3.599752
            div2 |  -.0450749   .3215522    -0.14   0.889    -.6753057    .5851558
            div3 |   .1910789   .8436223     0.23   0.821    -1.462391    1.844548
            div4 |   .0569874   .8503105     0.07   0.947     -1.60959    1.723565
            div5 |   -.154556   .7422256    -0.21   0.835    -1.609292     1.30018
            div6 |  -.0980926   .9515316    -0.10   0.918     -1.96306    1.766875
            div7 |  -.0082179   1.186896    -0.01   0.994    -2.334492    2.318056
            div8 |  -.4062348   1.928109    -0.21   0.833     -4.18526     3.37279
            div9 |  -.3619788   1.026448    -0.35   0.724     -2.37378    1.649823
elevat_range_msa |  -.0304974   .1640292    -0.19   0.853    -.3519887    .2909939
  ruggedness_msa |   7.818375   22.31704     0.35   0.726    -35.92223    51.55898
      heating_dd |  -.0200748   .0287376    -0.70   0.485    -.0763994    .0362499
      cooling_dd |  -.0327551   .0332701    -0.98   0.325    -.0979634    .0324532
          sprawl |   .0014495    .020203     0.07   0.943    -.0381477    .0410466
        _Iyear_2 |   .1726234   .2152309     0.80   0.423    -.2492213    .5944682
        _Iyear_3 |   .1563887   .5171774     0.30   0.762    -.8572604    1.170038
           _cons |   15.88022   9.466083     1.68   0.093    -2.672964     34.4334
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .7091371   .2113972     3.35   0.001     .2948063    1.123468
           l_pop |   .5729159   .1659942     3.45   0.001     .2475732    .8982585
   S_somecollege |   .8040024   .4183292     1.92   0.055    -.0159078    1.623913
   l_mean_income |  -.7335827   .3617183    -2.03   0.043    -1.442538   -.0246278
          S_poor |  -.5440808   .6546341    -0.83   0.406     -1.82714    .7389786
         S_manuf |   -.345667   .4895452    -0.71   0.480    -1.305158    .6138241
            div2 |  -.0593517   .1189435    -0.50   0.618    -.2924766    .1737732
            div3 |    .263113   .1234395     2.13   0.033      .021176    .5050499
            div4 |   .1198456   .1460467     0.82   0.412    -.1664006    .4060918
            div5 |   .0443892   .1471363     0.30   0.763    -.2439927    .3327711
            div6 |   .0646289    .162929     0.40   0.692     -.254706    .3839639
            div7 |   .0803259   .1575313     0.51   0.610    -.2284298    .3890816
            div8 |  -.0917758   .2025481    -0.45   0.650    -.4887628    .3052111
            div9 |  -.1878096   .1846185    -1.02   0.309    -.5496552    .1740361
elevat_range_msa |  -.0524338   .0672209    -0.78   0.435    -.1841843    .0793168
  ruggedness_msa |   5.409723    3.14133     1.72   0.085    -.7471707    11.56662
      heating_dd |  -.0162714   .0041136    -3.96   0.000    -.0243339   -.0082089
      cooling_dd |   -.030459   .0100454    -3.03   0.002    -.0501477   -.0107704
          sprawl |   .0026692   .0038023     0.70   0.483    -.0047833    .0101217
        _Iyear_2 |   .2509121   .1068673     2.35   0.019      .041456    .4603682
        _Iyear_3 |   .3296141   .1913431     1.72   0.085    -.0454115    .7046397
           _cons |   11.39056   3.448804     3.30   0.001     4.631028    18.15009
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .5410546   .1934072     2.80   0.005     .1619833    .9201258
           l_pop |   .6287377   .1530409     4.11   0.000      .328783    .9286924
   S_somecollege |   .7545828    .342694     2.20   0.028      .082915    1.426251
   l_mean_income |  -.4576926   .2909663    -1.57   0.116    -1.027976    .1125908
          S_poor |  -.7193484   .5608564    -1.28   0.200    -1.818607      .37991
         S_manuf |  -.7068177   .4700723    -1.50   0.133    -1.628142    .2145071
            div2 |  -.2345067   .1110633    -2.11   0.035    -.4521869   -.0168266
            div3 |   .1150438    .116594     0.99   0.324    -.1134762    .3435637
            div4 |  -.0173136   .1432443    -0.12   0.904    -.2980672      .26344
            div5 |  -.1458258   .1305523    -1.12   0.264    -.4017037     .110052
            div6 |  -.1272857   .1475076    -0.86   0.388    -.4163953     .161824
            div7 |  -.1243647   .1422377    -0.87   0.382    -.4031455    .1544162
            div8 |  -.1882158   .1857687    -1.01   0.311    -.5523158    .1758842
            div9 |  -.3471251   .1678291    -2.07   0.039     -.676064   -.0181862
elevat_range_msa |  -.0292621    .052537    -0.56   0.578    -.1322328    .0737086
  ruggedness_msa |   4.109178   2.551431     1.61   0.107    -.8915343     9.10989
      heating_dd |  -.0129302   .0036194    -3.57   0.000    -.0200241   -.0058362
      cooling_dd |  -.0191658   .0080032    -2.39   0.017    -.0348518   -.0034798
          sprawl |   .0056043   .0034433     1.63   0.104    -.0011444    .0123531
        _Iyear_2 |   .1720676   .0893415     1.93   0.054    -.0030385    .3471737
        _Iyear_3 |   .2036108   .1583179     1.29   0.198    -.1066865    .5139081
           _cons |   8.926156   2.718111     3.28   0.001     3.598756    14.25356
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .5541035   .1714871     3.23   0.001      .217995     .890212
           l_pop |   .6197222   .1385672     4.47   0.000     .3481354    .8913089
   S_somecollege |   .6151665   .4117069     1.49   0.135    -.1917642    1.422097
   l_mean_income |  -.1016439   .3150755    -0.32   0.747    -.7191806    .5158928
          S_poor |  -.4583411   .5783856    -0.79   0.428    -1.591956    .6752739
         S_manuf |  -.4019238   .6464503    -0.62   0.534    -1.668943    .8650955
            div2 |  -.1926973   .1103861    -1.75   0.081      -.40905    .0236554
            div3 |   .0689597   .1093146     0.63   0.528    -.1452931    .2832124
            div4 |   .0673687   .1615995     0.42   0.677    -.2493605    .3840978
            div5 |  -.0467057   .1299938    -0.36   0.719    -.3014889    .2080775
            div6 |  -.0713149     .13138    -0.54   0.587    -.3288149    .1861851
            div7 |  -.0233431   .1386998    -0.17   0.866    -.2951898    .2485036
            div8 |  -.0861644   .1849127    -0.47   0.641    -.4485866    .2762578
            div9 |  -.2510863   .1508613    -1.66   0.096     -.546769    .0445963
elevat_range_msa |  -.0282758   .0581126    -0.49   0.627    -.1421744    .0856229
  ruggedness_msa |   4.151312   2.325562     1.79   0.074    -.4067061    8.709331
      heating_dd |  -.0084511   .0041484    -2.04   0.042    -.0165819   -.0003203
      cooling_dd |  -.0093906   .0098465    -0.95   0.340    -.0286893    .0099082
          sprawl |   .0069066   .0035042     1.97   0.049     .0000385    .0137746
        _Iyear_2 |   .1927814   .0929574     2.07   0.038     .0105883    .3749744
        _Iyear_3 |   .2831736   .1679288     1.69   0.092    -.0459607    .6123079
           _cons |   5.080336   2.988383     1.70   0.089    -.7767864    10.93746
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           11.548                 2.640
Constant effect |            1.266                 2.333
Dominance       |            0.000                 2.436
Exogeneity      |           12.535                 2.803
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           11.548                 2.280
Constant effect |            1.266                 2.069
Dominance       |            0.000                 2.013
Exogeneity      |           12.535                 2.271
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_pix1835 ), robust                                      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   16337.06
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9523
                                                  Root MSE        =     .29916

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .7180332   .1398399     5.13   0.000      .443952    .9921144
           l_pop |   .3870944   .1310168     2.95   0.003     .1303061    .6438826
         l_pop80 |    .767765   .2677331     2.87   0.004     .2430177    1.292512
         l_pop70 |  -.5448421   .3205395    -1.70   0.089    -1.173088    .0834038
         l_pop60 |  -.0775066   .2763463    -0.28   0.779    -.6191353    .4641221
         l_pop50 |  -.2778799   .1863996    -1.49   0.136    -.6432164    .0874566
         l_pop40 |     .19416   .1916151     1.01   0.311    -.1813988    .5697187
         l_pop30 |   .0352811   .1596501     0.22   0.825    -.2776274    .3481895
         l_pop20 |   .0894539   .0853319     1.05   0.294    -.0777936    .2567014
   S_somecollege |   1.001442   .2787754     3.59   0.000     .4550518    1.547831
   l_mean_income |  -.6691001   .2138736    -3.13   0.002    -1.088285   -.2499155
          S_poor |  -.4620283   .3418003    -1.35   0.176    -1.131945     .207888
         S_manuf |  -.1503591    .314886    -0.48   0.633    -.7675244    .4668061
            div2 |   -.051918   .0987249    -0.53   0.599    -.2454152    .1415792
            div3 |   .2637617   .1242966     2.12   0.034     .0201449    .5073785
            div4 |   .1286454   .1240409     1.04   0.300    -.1144703    .3717611
            div5 |   .0137593   .1198512     0.11   0.909    -.2211447    .2486633
            div6 |  -.0122763   .1336843    -0.09   0.927    -.2742927    .2497401
            div7 |    .071578   .1322877     0.54   0.588    -.1877011    .3308572
            div8 |  -.0690025   .1802944    -0.38   0.702    -.4223731    .2843681
            div9 |  -.0876065   .1508688    -0.58   0.561    -.3833038    .2080908
elevat_range_msa |  -.0418052   .0295419    -1.42   0.157    -.0997063    .0160959
  ruggedness_msa |   3.775184   1.767174     2.14   0.033     .3115859    7.238782
      heating_dd |  -.0141978   .0023157    -6.13   0.000    -.0187366   -.0096591
      cooling_dd |  -.0231994   .0058737    -3.95   0.000    -.0347116   -.0116871
          sprawl |   .0046465   .0021892     2.12   0.034     .0003557    .0089373
        _Iyear_2 |   .2683958   .0572919     4.68   0.000     .1561058    .3806859
        _Iyear_3 |   .3735748   .1090142     3.43   0.001      .159911    .5872387
           _cons |   10.06898   2.043109     4.93   0.000      6.06456     14.0734
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 xi: ivqregress iqr l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_pix1835 ) ,  quantile(10 25 50 75 90)  ngrid(100) vce(robust) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Initial grid:
Quantile = 0.10: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.25: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.50: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.75: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.90: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.25: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.50: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.75: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.90: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done

IV quantile regression                               Number of obs  =      684
Estimator: Inverse quantile regression               Wald chi2(140) = 35630.57
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |    1.42585   .2398834     5.94   0.000     .9556871    1.896013
           l_pop |   .3041854   .1440622     2.11   0.035     .0218287    .5865422
         l_pop80 |  -.0355118   .5643687    -0.06   0.950    -1.141654    1.070631
         l_pop70 |   .5200274   .8232108     0.63   0.528    -1.093436    2.133491
         l_pop60 |  -.9903346   .7923445    -1.25   0.211    -2.543301    .5626321
         l_pop50 |    .167594   .5567512     0.30   0.763    -.9236183    1.258806
         l_pop40 |   .3847617   .2659496     1.45   0.148    -.1364898    .9060133
         l_pop30 |  -.1855498    .326234    -0.57   0.570    -.8249566    .4538571
         l_pop20 |  -.1026134   .2105537    -0.49   0.626     -.515291    .3100643
   S_somecollege |   .7593199      .5443     1.40   0.163    -.3074885    1.826128
   l_mean_income |  -.9404616   .4978747    -1.89   0.059    -1.916278    .0353548
          S_poor |  -.4699015   .5231851    -0.90   0.369    -1.495325    .5555224
         S_manuf |   .8376757   .4134359     2.03   0.043     .0273564    1.647995
            div2 |   .1965745   .4665982     0.42   0.674    -.7179413     1.11109
            div3 |   .3297396    .482674     0.68   0.495    -.6162841    1.275763
            div4 |   .2486864   .4564813     0.54   0.586    -.6460004    1.143373
            div5 |   .1669266   .5050203     0.33   0.741     -.822895    1.156748
            div6 |   .1670616   .4985611     0.34   0.738    -.8101001    1.144223
            div7 |   .0913461   .5290005     0.17   0.863    -.9454759    1.128168
            div8 |  -.3840458   .5515041    -0.70   0.486    -1.464974    .6968824
            div9 |   .2124358   .6212765     0.34   0.732    -1.005244    1.430115
elevat_range_msa |  -.1823867    .089899    -2.03   0.042    -.3585855   -.0061879
  ruggedness_msa |   10.37501   5.219791     1.99   0.047     .1444033    20.60561
      heating_dd |  -.0160663   .0045882    -3.50   0.000    -.0250589   -.0070737
      cooling_dd |  -.0273144   .0098676    -2.77   0.006    -.0466544   -.0079743
          sprawl |   .0019112   .0041346     0.46   0.644    -.0061924    .0100148
        _Iyear_2 |    .284446    .094739     3.00   0.003      .098761    .4701309
        _Iyear_3 |   .4414252   .1660453     2.66   0.008     .1159824     .766868
           _cons |   14.12247   4.717223     2.99   0.003     4.876886    23.36806
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.311852   .2238826     5.86   0.000     .8730498    1.750653
           l_pop |   .1685175   .2758802     0.61   0.541    -.3721978    .7092327
         l_pop80 |   .6031097    .611769     0.99   0.324    -.5959355    1.802155
         l_pop70 |   .0767963   .7704017     0.10   0.921    -1.433163    1.586756
         l_pop60 |  -1.059902   .7887606    -1.34   0.179    -2.605844    .4860407
         l_pop50 |   .1257537   .5912959     0.21   0.832    -1.033165    1.284672
         l_pop40 |    .248267   .4099321     0.61   0.545    -.5551851    1.051719
         l_pop30 |  -.0398829   .3222375    -0.12   0.901    -.6714567    .5916909
         l_pop20 |  -.0100176   .1732347    -0.06   0.954    -.3495514    .3295162
   S_somecollege |   .0936215   .6996585     0.13   0.894    -1.277684    1.464927
   l_mean_income |  -.3477516   .5945364    -0.58   0.559    -1.513021    .8175184
          S_poor |  -.3360512   .7391408    -0.45   0.649    -1.784741    1.112638
         S_manuf |   .5925421   .5490321     1.08   0.280    -.4835411    1.668625
            div2 |  -.1110091   .2614866    -0.42   0.671    -.6235135    .4014952
            div3 |   .0506192   .2493259     0.20   0.839    -.4380506    .5392891
            div4 |  -.0729837   .2755699    -0.26   0.791    -.6130907    .4671233
            div5 |   -.196208   .2548982    -0.77   0.441    -.6957993    .3033832
            div6 |  -.2662074   .2619978    -1.02   0.310    -.7797136    .2472988
            div7 |  -.1394654   .2735912    -0.51   0.610    -.6756943    .3967634
            div8 |  -.8406507   .3854892    -2.18   0.029    -1.596196   -.0851058
            div9 |  -.3650907    .325858    -1.12   0.263    -1.003761    .2735794
elevat_range_msa |  -.0515312   .0596954    -0.86   0.388     -.168532    .0654696
  ruggedness_msa |   8.896945   2.878628     3.09   0.002     3.254938    14.53895
      heating_dd |  -.0206649   .0054385    -3.80   0.000    -.0313241   -.0100057
      cooling_dd |  -.0359503   .0135529    -2.65   0.008    -.0625134   -.0093872
          sprawl |   .0001469   .0036159     0.04   0.968    -.0069402     .007234
        _Iyear_2 |   .4407692   .1413729     3.12   0.002     .1636834    .7178549
        _Iyear_3 |   .6426593   .2551176     2.52   0.012      .142638    1.142681
           _cons |   9.670721    5.23592     1.85   0.065    -.5914943    19.93294
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   1.068351    .352231     3.03   0.002     .3779913    1.758711
           l_pop |   .2447085    .186631     1.31   0.190    -.1210816    .6104986
         l_pop80 |   .3646672   .4780666     0.76   0.446    -.5723262    1.301661
         l_pop70 |   .3622719   .7246716     0.50   0.617    -1.058058    1.782602
         l_pop60 |  -.8811908   .7764345    -1.13   0.256    -2.402974    .6405928
         l_pop50 |   .1574353   .4336287     0.36   0.717    -.6924613    1.007332
         l_pop40 |  -.0054635   .3156426    -0.02   0.986    -.6241116    .6131846
         l_pop30 |  -.0372661   .2648512    -0.14   0.888     -.556365    .4818327
         l_pop20 |   .0416111   .2116032     0.20   0.844    -.3731235    .4563457
   S_somecollege |   -.115839   .7725862    -0.15   0.881     -1.63008    1.398402
   l_mean_income |  -.5614015   .2946386    -1.91   0.057    -1.138882    .0160795
          S_poor |  -.2834318   .6139573    -0.46   0.644    -1.486766    .9199025
         S_manuf |    .191318   .7093485     0.27   0.787     -1.19898    1.581616
            div2 |   -.298466   .0872498    -3.42   0.001    -.4694725   -.1274595
            div3 |  -.0103525   .1694127    -0.06   0.951    -.3423954    .3216904
            div4 |  -.1887812   .2279369    -0.83   0.408    -.6355294     .257967
            div5 |  -.2968987   .1850902    -1.60   0.109    -.6596688    .0658714
            div6 |  -.2700832   .2398859    -1.13   0.260    -.7402509    .2000845
            div7 |  -.2168157    .184619    -1.17   0.240    -.5786623    .1450309
            div8 |  -.6287275   .4077782    -1.54   0.123    -1.427958    .1705031
            div9 |  -.6276348    .344993    -1.82   0.069    -1.303809     .048539
elevat_range_msa |  -.0104019   .0527365    -0.20   0.844    -.1137636    .0929598
  ruggedness_msa |   6.172057    2.34007     2.64   0.008     1.585605    10.75851
      heating_dd |  -.0215821   .0064771    -3.33   0.001    -.0342771   -.0088872
      cooling_dd |  -.0440466   .0145908    -3.02   0.003    -.0726441   -.0154492
          sprawl |    -.00355   .0062761    -0.57   0.572    -.0158509    .0087509
        _Iyear_2 |    .386251   .1625697     2.38   0.018     .0676202    .7048818
        _Iyear_3 |   .6377271   .3039661     2.10   0.036     .0419643     1.23349
           _cons |   12.26388   2.538816     4.83   0.000     7.287896    17.23987
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .6058619   .3188227     1.90   0.057     -.019019    1.230743
           l_pop |    .371134   .2475436     1.50   0.134    -.1140425    .8563105
         l_pop80 |   .3365332   .4524695     0.74   0.457    -.5502907    1.223357
         l_pop70 |   .0005437   .4357369     0.00   0.999    -.8534849    .8545723
         l_pop60 |  -.2727835   .4409556    -0.62   0.536     -1.13704    .5914736
         l_pop50 |  -.1313217   .1898416    -0.69   0.489    -.5034044    .2407611
         l_pop40 |   .1961463   .2433854     0.81   0.420    -.2808803    .6731729
         l_pop30 |   .1518102   .2362674     0.64   0.521    -.3112654    .6148858
         l_pop20 |   -.041491   .1015728    -0.41   0.683    -.2405701     .157588
   S_somecollege |   .9975537   .7050058     1.41   0.157    -.3842322     2.37934
   l_mean_income |  -.3821436   .3457039    -1.11   0.269    -1.059711    .2954236
          S_poor |  -.4922658   1.006654    -0.49   0.625    -2.465272     1.48074
         S_manuf |  -.2734188   .6333157    -0.43   0.666    -1.514695    .9678572
            div2 |  -.2517001   .0866802    -2.90   0.004    -.4215901   -.0818101
            div3 |   .0678437   .1308734     0.52   0.604    -.1886635    .3243509
            div4 |   -.095457     .15736    -0.61   0.544    -.4038769    .2129629
            div5 |  -.1581671   .0871584    -1.81   0.070    -.3289944    .0126603
            div6 |  -.1625629    .115235    -1.41   0.158    -.3884194    .0632936
            div7 |   -.150238   .1055449    -1.42   0.155    -.3571022    .0566262
            div8 |  -.1119355   .2650499    -0.42   0.673    -.6314238    .4075527
            div9 |  -.2353292   .1545994    -1.52   0.128    -.5383385    .0676801
elevat_range_msa |  -.0160523   .0569225    -0.28   0.778    -.1276183    .0955137
  ruggedness_msa |   3.988382    2.63837     1.51   0.131    -1.182729    9.159492
      heating_dd |  -.0126159   .0036348    -3.47   0.001      -.01974   -.0054918
      cooling_dd |   -.012516   .0119597    -1.05   0.295    -.0359567    .0109247
          sprawl |   .0042642   .0049732     0.86   0.391     -.005483    .0140114
        _Iyear_2 |   .2269714   .2113678     1.07   0.283    -.1873019    .6412447
        _Iyear_3 |   .3297211   .3818683     0.86   0.388    -.4187271    1.078169
           _cons |   7.756976   2.740291     2.83   0.005     2.386105    13.12785
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .4287968   .1756318     2.44   0.015     .0845648    .7730287
           l_pop |   .7171573   .1588182     4.52   0.000     .4058793    1.028435
         l_pop80 |    .234632   .4195683     0.56   0.576    -.5877068    1.056971
         l_pop70 |  -.2594475   .5821157    -0.45   0.656    -1.400373    .8814783
         l_pop60 |  -.0367021   .4006474    -0.09   0.927    -.8219566    .7485523
         l_pop50 |  -.3342099   .2079017    -1.61   0.108    -.7416897      .07327
         l_pop40 |   .3593545   .2806092     1.28   0.200    -.1906296    .9093385
         l_pop30 |  -.2413756   .1941374    -1.24   0.214    -.6218779    .1391266
         l_pop20 |   .2623593   .1073088     2.44   0.014      .052038    .4726807
   S_somecollege |   1.006705   .5387418     1.87   0.062    -.0492094    2.062619
   l_mean_income |   .0518829   .2812282     0.18   0.854    -.4993143      .60308
          S_poor |  -.4592584   .5688731    -0.81   0.419    -1.574229    .6557124
         S_manuf |  -.6035705   .4957279    -1.22   0.223    -1.575179    .3680383
            div2 |  -.0898925   .0856401    -1.05   0.294    -.2577439     .077959
            div3 |   .1389534   .0740614     1.88   0.061    -.0062043    .2841111
            div4 |    .101069   .1145213     0.88   0.377    -.1233886    .3255266
            div5 |  -.0839465   .0833042    -1.01   0.314    -.2472197    .0793268
            div6 |  -.1132573   .0712773    -1.59   0.112    -.2529582    .0264436
            div7 |  -.0290167    .090439    -0.32   0.748    -.2062739    .1482406
            div8 |  -.0267568   .1613125    -0.17   0.868    -.3429235    .2894098
            div9 |  -.1986257   .1177406    -1.69   0.092    -.4293929    .0321416
elevat_range_msa |  -.0536967   .0432894    -1.24   0.215    -.1385423    .0311489
  ruggedness_msa |   5.226177   2.147527     2.43   0.015     1.017101    9.435253
      heating_dd |  -.0123311   .0033929    -3.63   0.000     -.018981   -.0056811
      cooling_dd |  -.0104755   .0063954    -1.64   0.101    -.0230103    .0020593
          sprawl |   .0060589    .002632     2.30   0.021     .0009003    .0112174
        _Iyear_2 |   .1335368   .1115224     1.20   0.231    -.0850431    .3521168
        _Iyear_3 |   .1910811   .2079963     0.92   0.358    -.2165843    .5987464
           _cons |   3.528489   2.541312     1.39   0.165    -1.452391     8.50937
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            6.882                 2.314
Constant effect |            3.789                 2.269
Dominance       |            0.000                 2.600
Exogeneity      |            2.217                 2.368
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            6.882                 2.082
Constant effect |            3.789                 2.144
Dominance       |            0.000                 2.260
Exogeneity      |            2.217                 2.161
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
.                 
. 
. log off 
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_2.log
  log type:  text
 paused on:  12 May 2023, 16:35:06
-------------------------------------------------------------------------------------
